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optimization method</title> <meta name="description" content="Search results for: recovery optimization method"> <meta name="keywords" content="recovery optimization method"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="recovery optimization method"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 22400</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: recovery optimization method</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">22400</span> Enhancing the Resilience of Combat System-Of-Systems Under Certainty and Uncertainty: Two-Phase Resilience Optimization Model and Deep Reinforcement Learning-Based Recovery Optimization Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Xueming%20Xu">Xueming Xu</a>, <a href="https://publications.waset.org/abstracts/search?q=Jiahao%20Liu"> Jiahao Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Jichao%20Li"> Jichao Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Kewei%20Yang"> Kewei Yang</a>, <a href="https://publications.waset.org/abstracts/search?q=Minghao%20Li"> Minghao Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Bingfeng%20Ge"> Bingfeng Ge</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A combat system-of-systems (CSoS) comprises various types of functional combat entities that interact to meet corresponding task requirements in the present and future. Enhancing the resilience of CSoS holds significant military value in optimizing the operational planning process, improving military survivability, and ensuring the successful completion of operational tasks. Accordingly, this research proposes an integrated framework called CSoS resilience enhancement (CSoSRE) to enhance the resilience of CSoS from a recovery perspective. Specifically, this research presents a two-phase resilience optimization model to define a resilience optimization objective for CSoS. This model considers not only task baseline, recovery cost, and recovery time limit but also the characteristics of emergency recovery and comprehensive recovery. Moreover, the research extends it from the deterministic case to the stochastic case to describe the uncertainty in the recovery process. Based on this, a resilience-oriented recovery optimization method based on deep reinforcement learning (RRODRL) is proposed to determine a set of entities requiring restoration and their recovery sequence, thereby enhancing the resilience of CSoS. This method improves the deep Q-learning algorithm by designing a discount factor that adapts to changes in CSoS state at different phases, simultaneously considering the network’s structural and functional characteristics within CSoS. Finally, extensive experiments are conducted to test the feasibility, effectiveness and superiority of the proposed framework. The obtained results offer useful insights for guiding operational recovery activity and designing a more resilient CSoS. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=combat%20system-of-systems" title="combat system-of-systems">combat system-of-systems</a>, <a href="https://publications.waset.org/abstracts/search?q=resilience%20optimization%20model" title=" resilience optimization model"> resilience optimization model</a>, <a href="https://publications.waset.org/abstracts/search?q=recovery%20optimization%20method" title=" recovery optimization method"> recovery optimization method</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20reinforcement%20learning" title=" deep reinforcement learning"> deep reinforcement learning</a>, <a href="https://publications.waset.org/abstracts/search?q=certainty%20and%20uncertainty" title=" certainty and uncertainty"> certainty and uncertainty</a> </p> <a href="https://publications.waset.org/abstracts/193327/enhancing-the-resilience-of-combat-system-of-systems-under-certainty-and-uncertainty-two-phase-resilience-optimization-model-and-deep-reinforcement-learning-based-recovery-optimization-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/193327.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">16</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">22399</span> Study of the Stability of Underground Mines by Numerical Method: The Mine Chaabet El Hamra, Algeria</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nakache%20Radouane">Nakache Radouane</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Boukelloul"> M. Boukelloul</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Fredj"> M. Fredj</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Method room and pillar sizes are key factors for safe mining and their recovery in open-stop mining. This method is advantageous due to its simplicity and requirement of little information to be used. It is probably the most representative method among the total load approach methods although it also remains a safe design method. Using a finite element software (PLAXIS 3D), analyses were carried out with an elasto-plastic model and comparisons were made with methods based on the total load approach. The results were presented as the optimization for improving the ore recovery rate while maintaining a safe working environment. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=room%20and%20pillar" title="room and pillar">room and pillar</a>, <a href="https://publications.waset.org/abstracts/search?q=mining" title=" mining"> mining</a>, <a href="https://publications.waset.org/abstracts/search?q=total%20load%20approach" title=" total load approach"> total load approach</a>, <a href="https://publications.waset.org/abstracts/search?q=elasto-plastic" title=" elasto-plastic"> elasto-plastic</a> </p> <a href="https://publications.waset.org/abstracts/48348/study-of-the-stability-of-underground-mines-by-numerical-method-the-mine-chaabet-el-hamra-algeria" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/48348.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">330</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">22398</span> CSoS-STRE: A Combat System-of-System Space-Time Resilience Enhancement Framework</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jiuyao%20Jiang">Jiuyao Jiang</a>, <a href="https://publications.waset.org/abstracts/search?q=Jiahao%20Liu"> Jiahao Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Jichao%20Li"> Jichao Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Kewei%20Yang"> Kewei Yang</a>, <a href="https://publications.waset.org/abstracts/search?q=Minghao%20Li"> Minghao Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Bingfeng%20Ge"> Bingfeng Ge</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Modern warfare has transitioned from the paradigm of isolated combat forces to system-to-system confrontations due to advancements in combat technologies and application concepts. A combat system-of-systems (CSoS) is a combat network composed of independently operating entities that interact with one another to provide overall operational capabilities. Enhancing the resilience of CSoS is garnering increasing attention due to its significant practical value in optimizing network architectures, improving network security and refining operational planning. Accordingly, a unified framework called CSoS space-time resilience enhancement (CSoS-STRE) has been proposed, which enhances the resilience of CSoS by incorporating spatial features. Firstly, a multilayer spatial combat network model has been constructed, which incorporates an information layer depicting the interrelations among combat entities based on the OODA loop, along with a spatial layer that considers the spatial characteristics of equipment entities, thereby accurately reflecting the actual combat process. Secondly, building upon the combat network model, a spatiotemporal resilience optimization model is proposed, which reformulates the resilience optimization problem as a classical linear optimization model with spatial features. Furthermore, the model is extended from scenarios without obstacles to those with obstacles, thereby further emphasizing the importance of spatial characteristics. Thirdly, a resilience-oriented recovery optimization method based on improved non dominated sorting genetic algorithm II (R-INSGA) is proposed to determine the optimal recovery sequence for the damaged entities. This method not only considers spatial features but also provides the optimal travel path for multiple recovery teams. Finally, the feasibility, effectiveness, and superiority of the CSoS-STRE are demonstrated through a case study. Simultaneously, under deliberate attack conditions based on degree centrality and maximum operational loop performance, the proposed CSoS-STRE method is compared with six baseline recovery strategies, which are based on performance, time, degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality. The comparison demonstrates that CSoS-STRE achieves faster convergence and superior performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=space-time%20resilience%20enhancement" title="space-time resilience enhancement">space-time resilience enhancement</a>, <a href="https://publications.waset.org/abstracts/search?q=resilience%20optimization%20model" title=" resilience optimization model"> resilience optimization model</a>, <a href="https://publications.waset.org/abstracts/search?q=combat%20system-of-systems" title=" combat system-of-systems"> combat system-of-systems</a>, <a href="https://publications.waset.org/abstracts/search?q=recovery%20optimization%20method" title=" recovery optimization method"> recovery optimization method</a>, <a href="https://publications.waset.org/abstracts/search?q=no-obstacles%20and%20obstacles" title=" no-obstacles and obstacles"> no-obstacles and obstacles</a> </p> <a href="https://publications.waset.org/abstracts/193217/csos-stre-a-combat-system-of-system-space-time-resilience-enhancement-framework" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/193217.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">15</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">22397</span> The Initiator Matters in Service Co-Recovery: Investigation on Attribution and Satisfaction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chia-Ching%20Tsai">Chia-Ching Tsai</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the literature, the positive effect of service co-recovery has been evidenced, and which customers’ attribution is the key successful factor has also been indicated. There is also literature investigating on initiation of co-recovery for finding out the superior way to co-recovery, and indicating co-recovery initiated by employees causes better effect of co-recovery. This research postulates the consequences of co-recovery by different initiators affect customers’ attribution and the resultant results. Thus, this research uses a 3x2 factorial design to investigate the impact of initiator of co-recovery and consequence of co-recovery on customers’ attribution and post-recovery satisfaction. The results show initiation of co-recovery has a significant influence on internal attribution, and the employee initiator causes the highest internal attribution. The consequences of co-recovery interact with initiators of co-recovery on internal attribution significantly. Moreover, internal attribution significantly affects post-recovery satisfaction. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=service%20co-recovery" title="service co-recovery">service co-recovery</a>, <a href="https://publications.waset.org/abstracts/search?q=initiation%20of%20co-recovery" title=" initiation of co-recovery"> initiation of co-recovery</a>, <a href="https://publications.waset.org/abstracts/search?q=attribution" title=" attribution"> attribution</a>, <a href="https://publications.waset.org/abstracts/search?q=post-recovery%20satisfaction" title=" post-recovery satisfaction"> post-recovery satisfaction</a> </p> <a href="https://publications.waset.org/abstracts/78185/the-initiator-matters-in-service-co-recovery-investigation-on-attribution-and-satisfaction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/78185.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">263</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">22396</span> Optimization of Supercritical CO2 Power Cycle for Waste Heat Recovery from Gas Turbine with Respect to Cooling Condition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Young%20Min%20Kim">Young Min Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Jeong%20Lak%20Sohn"> Jeong Lak Sohn</a>, <a href="https://publications.waset.org/abstracts/search?q=Eui%20Soo%20Yoon"> Eui Soo Yoon</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study describes the optimization of supercritical carbon dioxide (S-CO2) power cycle for recovering waste heat from a gas turbine. An S-CO2 cycle that recovers heat from small industrial and aeroderivative gas turbines can outperform a steam-bottoming cycle despite its simplicity and compactness. In using S-CO2 power cycles for waste heat recovery, a split cycle was studied to maximize the net output power by incorporating the utilization efficiency of the waste heat (lowering the temperature of the exhaust gas through the heater) along with the thermal efficiency of the cycle (minimizing the temperature difference for the heat transfer, exergy loss). The cooling condition of the S-CO2 WHR system has a great impact on the performance and the optimum low pressure of the system. Furthermore, the optimum high pressure of the S-CO2 WHR systems for the maximum power from the given heat sources is dependent on the temperature of the waste heat source. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=exergy%20loss" title="exergy loss">exergy loss</a>, <a href="https://publications.waset.org/abstracts/search?q=gas%20turbine" title=" gas turbine"> gas turbine</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=supercritical%20CO2%20power%20cycle" title=" supercritical CO2 power cycle"> supercritical CO2 power cycle</a>, <a href="https://publications.waset.org/abstracts/search?q=split%20cycle" title=" split cycle"> split cycle</a>, <a href="https://publications.waset.org/abstracts/search?q=waste%20heat%20recovery" title=" waste heat recovery"> waste heat recovery</a> </p> <a href="https://publications.waset.org/abstracts/59112/optimization-of-supercritical-co2-power-cycle-for-waste-heat-recovery-from-gas-turbine-with-respect-to-cooling-condition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/59112.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">349</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">22395</span> Thermodynamic Modeling of Three Pressure Level Reheat HRSG, Parametric Analysis and Optimization Using PSO</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mahmoud%20Nadir">Mahmoud Nadir</a>, <a href="https://publications.waset.org/abstracts/search?q=Adel%20Ghenaiet"> Adel Ghenaiet</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The main purpose of this study is the thermodynamic modeling, the parametric analysis, and the optimization of three pressure level reheat HRSG (Heat Recovery Steam Generator) using PSO method (Particle Swarm Optimization). In this paper, a parametric analysis followed by a thermodynamic optimization is presented. The chosen objective function is the specific work of the steam cycle that may be, in the case of combined cycle (CC), a good criterion of thermodynamic performance analysis, contrary to the conventional steam turbines in which the thermal efficiency could be also an important criterion. The technologic constraints such as maximal steam cycle temperature, minimal steam fraction at steam turbine outlet, maximal steam pressure, minimal stack temperature, minimal pinch point, and maximal superheater effectiveness are also considered. The parametric analyses permitted to understand the effect of design parameters and the constraints on steam cycle specific work variation. PSO algorithm was used successfully in HRSG optimization, knowing that the achieved results are in accordance with those of the previous studies in which genetic algorithms were used. Moreover, this method is easy to implement comparing with the other methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=combined%20cycle" title="combined cycle">combined cycle</a>, <a href="https://publications.waset.org/abstracts/search?q=HRSG%20thermodynamic%20modeling" title=" HRSG thermodynamic modeling"> HRSG thermodynamic modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=PSO" title=" PSO"> PSO</a>, <a href="https://publications.waset.org/abstracts/search?q=steam%20cycle%20specific%20work" title=" steam cycle specific work"> steam cycle specific work</a> </p> <a href="https://publications.waset.org/abstracts/38513/thermodynamic-modeling-of-three-pressure-level-reheat-hrsg-parametric-analysis-and-optimization-using-pso" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/38513.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">382</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">22394</span> Oil Recovery Study by Low Temperature Carbon Dioxide Injection in High-Pressure High-Temperature Micromodels</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zakaria%20Hamdi">Zakaria Hamdi</a>, <a href="https://publications.waset.org/abstracts/search?q=Mariyamni%20Awang"> Mariyamni Awang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> For the past decades, CO<sub>2</sub> flooding has been used as a successful method for enhanced oil recovery (EOR). However, high mobility ratio and fingering effect are considered as important drawbacka of this process. Low temperature injection of CO<sub>2</sub> into high temperature reservoirs may improve the oil recovery, but simulating multiphase flow in the non-isothermal medium is difficult, and commercial simulators are very unstable in these conditions. Furthermore, to best of authors’ knowledge, no experimental work was done to verify the results of the simulations and to understand the pore-scale process. In this paper, we present results of investigations on injection of low temperature CO<sub>2</sub> into a high-pressure high-temperature micromodel with injection temperature range from 34 to 75 °F. Effect of temperature and saturation changes of different fluids are measured in each case. The results prove the proposed method. The injection of CO<sub>2</sub> at low temperatures increased the oil recovery in high temperature reservoirs significantly. Also, CO<sub>2</sub> rich phases available in the high temperature system can affect the oil recovery through the better sweep of the oil which is initially caused by penetration of LCO<sub>2</sub> inside the system. Furthermore, no unfavorable effect was detected using this method. Low temperature CO<sub>2</sub> is proposed to be used as early as secondary recovery. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=enhanced%20oil%20recovery" title="enhanced oil recovery">enhanced oil recovery</a>, <a href="https://publications.waset.org/abstracts/search?q=CO%E2%82%82%20flooding" title=" CO₂ flooding"> CO₂ flooding</a>, <a href="https://publications.waset.org/abstracts/search?q=micromodel%20studies" title=" micromodel studies"> micromodel studies</a>, <a href="https://publications.waset.org/abstracts/search?q=miscible%20flooding" title=" miscible flooding"> miscible flooding</a> </p> <a href="https://publications.waset.org/abstracts/71727/oil-recovery-study-by-low-temperature-carbon-dioxide-injection-in-high-pressure-high-temperature-micromodels" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/71727.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">352</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">22393</span> The Impact of Level and Consequence of Service Co-Recovery on Post-Recovery Satisfaction and Repurchase Intent</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chia-Ching%20Tsai">Chia-Ching Tsai</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In service delivery, interpersonal interaction is the key to customer satisfaction, and apparently, the factor of human is critical in service delivery. Besides, customers quite care about the consequences of co-recovery. Thus, this research focuses on service failure caused by other customers and uses a 2x2 factorial design to investigate the impact of consequence and level of service co-recovery on post-recovery satisfaction and repurchase intent. 150 undergraduates were recruited as participants, and assigned to one of the four cells randomly. Every participant was requested to read the scenario and then rated the post-recovery satisfaction and repurchase intent. The results show that under the condition of failed co-recovery, level of co-recovery has no effect on post-recovery satisfaction, while under the condition of successful co-recovery, high-level co-recovery causes significantly higher post-recovery satisfaction than low-level co-recovery. Moreover, post-recovery satisfaction has significantly positive impact on repurchase intent. In the system of service delivery, customers interact with other customers frequently. Therefore, comparing with the literature, this research focuses on the service failure caused by other customers. This research also supplies a better understanding of customers’ view on consequences of different levels of co-recovery, which is helpful for the practitioners to make use of co-recovery. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=service%20failure" title="service failure">service failure</a>, <a href="https://publications.waset.org/abstracts/search?q=service%20co-recovery" title=" service co-recovery"> service co-recovery</a>, <a href="https://publications.waset.org/abstracts/search?q=consequence%20of%20co-recovery" title=" consequence of co-recovery"> consequence of co-recovery</a>, <a href="https://publications.waset.org/abstracts/search?q=level%20of%20co-recovery" title=" level of co-recovery"> level of co-recovery</a>, <a href="https://publications.waset.org/abstracts/search?q=post-recovery%20satisfaction" title=" post-recovery satisfaction"> post-recovery satisfaction</a>, <a href="https://publications.waset.org/abstracts/search?q=repurchase%20intent" title=" repurchase intent"> repurchase intent</a> </p> <a href="https://publications.waset.org/abstracts/78948/the-impact-of-level-and-consequence-of-service-co-recovery-on-post-recovery-satisfaction-and-repurchase-intent" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/78948.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">420</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">22392</span> Cuckoo Search (CS) Optimization Algorithm for Solving Constrained Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sait%20Ali%20Uymaz">Sait Ali Uymaz</a>, <a href="https://publications.waset.org/abstracts/search?q=G%C3%BClay%20Tezel"> Gülay Tezel</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents the comparison results on the performance of the Cuckoo Search (CS) algorithm for constrained optimization problems. For constraint handling, CS algorithm uses penalty method. CS algorithm is tested on thirteen well-known test problems and the results obtained are compared to Particle Swarm Optimization (PSO) algorithm. Mean, best, median and worst values were employed for the analyses of performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cuckoo%20search" title="cuckoo search">cuckoo search</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=constrained%20optimization%20problems" title=" constrained optimization problems"> constrained optimization problems</a>, <a href="https://publications.waset.org/abstracts/search?q=penalty%20method" title=" penalty method"> penalty method</a> </p> <a href="https://publications.waset.org/abstracts/13991/cuckoo-search-cs-optimization-algorithm-for-solving-constrained-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13991.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">558</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">22391</span> Periodic Topology and Size Optimization Design of Tower Crane Boom</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wu%20Qinglong">Wu Qinglong</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhou%20Qicai"> Zhou Qicai</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiong%20Xiaolei"> Xiong Xiaolei</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhang%20Richeng"> Zhang Richeng </a> </p> <p class="card-text"><strong>Abstract:</strong></p> In order to achieve the layout and size optimization of the web members of tower crane boom, a truss topology and cross section size optimization method based on continuum is proposed considering three typical working conditions. Firstly, the optimization model is established by replacing web members with web plates. And the web plates are divided into several sub-domains so that periodic soft kill option (SKO) method can be carried out for topology optimization of the slender boom. After getting the optimized topology of web plates, the optimized layout of web members is formed through extracting the principal stress distribution. Finally, using the web member radius as design variable, the boom compliance as objective and the material volume of the boom as constraint, the cross section size optimization mathematical model is established. The size optimization criterion is deduced from the mathematical model by Lagrange multiplier method and Kuhn-Tucker condition. By comparing the original boom with the optimal boom, it is identified that this optimization method can effectively lighten the boom and improve its performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=tower%20crane%20boom" title="tower crane boom">tower crane boom</a>, <a href="https://publications.waset.org/abstracts/search?q=topology%20optimization" title=" topology optimization"> topology optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=size%20optimization" title=" size optimization"> size optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=periodic" title=" periodic"> periodic</a>, <a href="https://publications.waset.org/abstracts/search?q=SKO" title=" SKO"> SKO</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization%20criterion" title=" optimization criterion"> optimization criterion</a> </p> <a href="https://publications.waset.org/abstracts/74618/periodic-topology-and-size-optimization-design-of-tower-crane-boom" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/74618.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">554</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">22390</span> Evaluation of a Surrogate Based Method for Global Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=David%20Lindstr%C3%B6m">David Lindström</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We evaluate the performance of a numerical method for global optimization of expensive functions. The method is using a response surface to guide the search for the global optimum. This metamodel could be based on radial basis functions, kriging, or a combination of different models. We discuss how to set the cycling parameters of the optimization method to get a balance between local and global search. We also discuss the eventual problem with Runge oscillations in the response surface. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=expensive%20function" title="expensive function">expensive function</a>, <a href="https://publications.waset.org/abstracts/search?q=infill%20sampling%20criterion" title=" infill sampling criterion"> infill sampling criterion</a>, <a href="https://publications.waset.org/abstracts/search?q=kriging" title=" kriging"> kriging</a>, <a href="https://publications.waset.org/abstracts/search?q=global%20optimization" title=" global optimization"> global optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=response%20surface" title=" response surface"> response surface</a>, <a href="https://publications.waset.org/abstracts/search?q=Runge%20phenomenon" title=" Runge phenomenon"> Runge phenomenon</a> </p> <a href="https://publications.waset.org/abstracts/24538/evaluation-of-a-surrogate-based-method-for-global-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24538.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">578</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">22389</span> Autonomic Recovery Plan with Server Virtualization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Hameed">S. Hameed</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Anwer"> S. Anwer</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Saad"> M. Saad</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Saady"> M. Saady</a> </p> <p class="card-text"><strong>Abstract:</strong></p> For autonomic recovery with server virtualization, a cogent plan that includes recovery techniques and backups with virtualized servers can be developed instead of assigning an idle server to backup operations. In addition to hardware cost reduction and data center trail, the disaster recovery plan can ensure system uptime and to meet objectives of high availability, recovery time, recovery point, server provisioning, and quality of services. This autonomic solution would also support disaster management, testing, and development of the recovery site. In this research, a workflow plan is proposed for supporting disaster recovery with virtualization providing virtual monitoring, requirements engineering, solution decision making, quality testing, and disaster management. This recovery model would make disaster recovery a lot easier, faster, and less error prone. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=autonomous%20intelligence" title="autonomous intelligence">autonomous intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=disaster%20recovery" title=" disaster recovery"> disaster recovery</a>, <a href="https://publications.waset.org/abstracts/search?q=cloud%20computing" title=" cloud computing"> cloud computing</a>, <a href="https://publications.waset.org/abstracts/search?q=server%20virtualization" title=" server virtualization"> server virtualization</a> </p> <a href="https://publications.waset.org/abstracts/129654/autonomic-recovery-plan-with-server-virtualization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/129654.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">162</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">22388</span> The Comparison and Optimization of the Analytic Method for Canthaxanthin, Food Colorants</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hee-Jae%20Suh">Hee-Jae Suh</a>, <a href="https://publications.waset.org/abstracts/search?q=Kyung-Su%20Kim"> Kyung-Su Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Min-Ji%20Kim"> Min-Ji Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Yeon-Seong%20Jeong"> Yeon-Seong Jeong</a>, <a href="https://publications.waset.org/abstracts/search?q=Ok-Hwan%20Lee"> Ok-Hwan Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Jae-Wook%20Shin"> Jae-Wook Shin</a>, <a href="https://publications.waset.org/abstracts/search?q=Hyang-Sook%20Chun"> Hyang-Sook Chun</a>, <a href="https://publications.waset.org/abstracts/search?q=Chan%20Lee"> Chan Lee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Canthaxanthin is keto-carotenoid produced from beta-carotene and it has been approved to be used in many countries as a food coloring agent. Canthaxanthin has been analyzed using High Performance Liquid Chromatography (HPLC) system with various ways of pretreatment methods. Four official methods for verification of canthaxanthin at FSA (UK), AOAC (US), EFSA (EU) and MHLW (Japan) were compared to improve its analytical and the pretreatment method. The Linearity, the limit of detection (LOD), the limit of quantification (LOQ), the accuracy, the precision and the recovery ratio were determined from each method with modification in pretreatment method. All HPLC methods exhibited correlation coefficients of calibration curves for canthaxanthin as 0.9999. The analysis methods from FSA, AOAC, and MLHW showed the LOD of 0.395 ppm, 0.105 ppm, and 0.084 ppm, and the LOQ of 1.196 ppm, 0.318 ppm, 0.254 ppm, respectively. Among tested methods, HPLC method of MHLW with modification in pretreatments was finally selected for the analysis of canthaxanthin in lab, because it exhibited the resolution factor of 4.0 and the selectivity of 1.30. This analysis method showed a correlation coefficients value of 0.9999 and the lowest LOD and LOQ. Furthermore, the precision ratio was lower than 1 and the accuracy was almost 100%. The method presented the recovery ratio of 90-110% with modification in pretreatment method. The cross-validation of coefficient variation was 5 or less among tested three institutions in Korea. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=analytic%20method" title="analytic method">analytic method</a>, <a href="https://publications.waset.org/abstracts/search?q=canthaxanthin" title=" canthaxanthin"> canthaxanthin</a>, <a href="https://publications.waset.org/abstracts/search?q=food%20colorants" title=" food colorants"> food colorants</a>, <a href="https://publications.waset.org/abstracts/search?q=pretreatment%20method" title=" pretreatment method"> pretreatment method</a> </p> <a href="https://publications.waset.org/abstracts/24563/the-comparison-and-optimization-of-the-analytic-method-for-canthaxanthin-food-colorants" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24563.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">683</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">22387</span> Curve Fitting by Cubic Bezier Curves Using Migrating Birds Optimization Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mitat%20Uysal">Mitat Uysal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A new met heuristic optimization algorithm called as Migrating Birds Optimization is used for curve fitting by rational cubic Bezier Curves. This requires solving a complicated multivariate optimization problem. In this study, the solution of this optimization problem is achieved by Migrating Birds Optimization algorithm that is a powerful met heuristic nature-inspired algorithm well appropriate for optimization. The results of this study show that the proposed method performs very well and being able to fit the data points to cubic Bezier Curves with a high degree of accuracy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=algorithms" title="algorithms">algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=Bezier%20curves" title=" Bezier curves"> Bezier curves</a>, <a href="https://publications.waset.org/abstracts/search?q=heuristic%20optimization" title=" heuristic optimization"> heuristic optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=migrating%20birds%20optimization" title=" migrating birds optimization"> migrating birds optimization</a> </p> <a href="https://publications.waset.org/abstracts/78026/curve-fitting-by-cubic-bezier-curves-using-migrating-birds-optimization-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/78026.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">337</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">22386</span> Application of a Modified Crank-Nicolson Method in Metallurgy</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kobamelo%20Mashaba">Kobamelo Mashaba</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The molten slag has a high substantial temperatures range between 1723-1923, carrying a huge amount of useful energy for reducing energy consumption and CO₂ emissions under the heat recovery process. Therefore in this study, we investigated the performance of the modified crank Nicolson method for a delayed partial differential equation on the heat recovery of molten slag in the metallurgical mining environment. It was proved that the proposed method converges quickly compared to the classic method with the existence of a unique solution. It was inferred from numerical result that the proposed methodology is more viable and profitable for the mining industry. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=delayed%20partial%20differential%20equation" title="delayed partial differential equation">delayed partial differential equation</a>, <a href="https://publications.waset.org/abstracts/search?q=modified%20Crank-Nicolson%20Method" title=" modified Crank-Nicolson Method"> modified Crank-Nicolson Method</a>, <a href="https://publications.waset.org/abstracts/search?q=molten%20slag" title=" molten slag"> molten slag</a>, <a href="https://publications.waset.org/abstracts/search?q=heat%20recovery" title=" heat recovery"> heat recovery</a>, <a href="https://publications.waset.org/abstracts/search?q=parabolic%20equation" title=" parabolic equation"> parabolic equation</a> </p> <a href="https://publications.waset.org/abstracts/152073/application-of-a-modified-crank-nicolson-method-in-metallurgy" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/152073.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">101</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">22385</span> Influence of Optimization Method on Parameters Identification of Hyperelastic Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bale%20Baidi%20Blaise">Bale Baidi Blaise</a>, <a href="https://publications.waset.org/abstracts/search?q=Gilles%20Marckmann"> Gilles Marckmann</a>, <a href="https://publications.waset.org/abstracts/search?q=Liman%20%20Kaoye"> Liman Kaoye</a>, <a href="https://publications.waset.org/abstracts/search?q=Talaka%20Dya"> Talaka Dya</a>, <a href="https://publications.waset.org/abstracts/search?q=Moustapha%20Bachirou"> Moustapha Bachirou</a>, <a href="https://publications.waset.org/abstracts/search?q=Gambo%20Betchewe"> Gambo Betchewe</a>, <a href="https://publications.waset.org/abstracts/search?q=Tibi%20Beda"> Tibi Beda</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This work highlights the capabilities of particles swarm optimization (PSO) method to identify parameters of hyperelastic models. The study compares this method with Genetic Algorithm (GA) method, Least Squares (LS) method, Pattern Search Algorithm (PSA) method, Beda-Chevalier (BC) method and the Levenberg-Marquardt (LM) method. Four classic hyperelastic models are used to test the diﬀerent methods through parameters identification. Then, the study compares the ability of these models to reproduce experimental Treloar data in simple tension, biaxial tension and pure shear. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title="particle swarm optimization">particle swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=identification" title=" identification"> identification</a>, <a href="https://publications.waset.org/abstracts/search?q=hyperelastic" title=" hyperelastic"> hyperelastic</a>, <a href="https://publications.waset.org/abstracts/search?q=model" title=" model"> model</a> </p> <a href="https://publications.waset.org/abstracts/138255/influence-of-optimization-method-on-parameters-identification-of-hyperelastic-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/138255.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">171</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">22384</span> Global Optimization: The Alienor Method Mixed with Piyavskii-Shubert Technique</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Guettal%20Djaouida">Guettal Djaouida</a>, <a href="https://publications.waset.org/abstracts/search?q=Ziadi%20Abdelkader"> Ziadi Abdelkader</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we study a coupling of the Alienor method with the algorithm of Piyavskii-Shubert. The classical multidimensional global optimization methods involves great difficulties for their implementation to high dimensions. The Alienor method allows to transform a multivariable function into a function of a single variable for which it is possible to use efficient and rapid method for calculating the the global optimum. This simplification is based on the using of a reducing transformation called Alienor. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=global%20optimization" title="global optimization">global optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=reducing%20transformation" title=" reducing transformation"> reducing transformation</a>, <a href="https://publications.waset.org/abstracts/search?q=%CE%B1-dense%20curves" title=" α-dense curves"> α-dense curves</a>, <a href="https://publications.waset.org/abstracts/search?q=Alienor%20method" title=" Alienor method"> Alienor method</a>, <a href="https://publications.waset.org/abstracts/search?q=Piyavskii-Shubert%20algorithm" title=" Piyavskii-Shubert algorithm"> Piyavskii-Shubert algorithm</a> </p> <a href="https://publications.waset.org/abstracts/6505/global-optimization-the-alienor-method-mixed-with-piyavskii-shubert-technique" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/6505.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">503</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">22383</span> Gas Lift Optimization to Improve Well Performance</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20A.%20G.%20H.%20Abdalsadig">Mohamed A. G. H. Abdalsadig</a>, <a href="https://publications.waset.org/abstracts/search?q=Amir%20Nourian"> Amir Nourian</a>, <a href="https://publications.waset.org/abstracts/search?q=G.%20G.%20Nasr"> G. G. Nasr</a>, <a href="https://publications.waset.org/abstracts/search?q=Meisam%20Babaie"> Meisam Babaie</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Gas lift optimization is becoming more important now a day in petroleum industry. A proper lift optimization can reduce the operating cost, increase the net present value (NPV) and maximize the recovery from the asset. A widely accepted definition of gas lift optimization is to obtain the maximum output under specified operating conditions. In addition, gas lift, a costly and indispensable means to recover oil from high depth reservoir entails solving the gas lift optimization problems. Gas lift optimization is a continuous process; there are two levels of production optimization. The total field optimization involves optimizing the surface facilities and the injection rate that can be achieved by standard tools softwares. Well level optimization can be achieved by optimizing the well parameters such as point of injection, injection rate, and injection pressure. All these aspects have been investigated and presented in this study by using experimental data and PROSPER simulation program. The results show that the well head pressure has a large influence on the gas lift performance and also proved that smart gas lift valve can be used to improve gas lift performance by controlling gas injection from down hole. Obtaining the optimum gas injection rate is important because excessive gas injection reduces production rate and consequently increases the operation cost. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=optimization" title="optimization">optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=production%20rate" title=" production rate"> production rate</a>, <a href="https://publications.waset.org/abstracts/search?q=reservoir%20pressure%20effect" title=" reservoir pressure effect"> reservoir pressure effect</a>, <a href="https://publications.waset.org/abstracts/search?q=gas%20injection%20rate%20effect" title=" gas injection rate effect"> gas injection rate effect</a>, <a href="https://publications.waset.org/abstracts/search?q=gas%20injection%20pressure" title=" gas injection pressure"> gas injection pressure</a> </p> <a href="https://publications.waset.org/abstracts/46454/gas-lift-optimization-to-improve-well-performance" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/46454.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">413</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">22382</span> A Hybrid Derivative-Free Optimization Method for Pass Schedule Calculation in Cold Rolling Mill</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammadhadi%20Mirmohammadi">Mohammadhadi Mirmohammadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Reza%20Safian"> Reza Safian</a>, <a href="https://publications.waset.org/abstracts/search?q=Hossein%20Haddad"> Hossein Haddad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents an innovative solution for complex multi-objective optimization problem which is a part of efforts toward maximizing rolling mill throughput and minimizing processing costs in tandem cold rolling. This computational intelligence based optimization has been applied to the rolling schedules of tandem cold rolling mill. This method involves the combination of two derivative-free optimization procedures in the form of nested loops. The first optimization loop is based on Improving Hit and Run method which focus on balance of power, force and reduction distribution in rolling schedules. The second loop is a real-coded genetic algorithm based optimization procedure which optimizes energy consumption and productivity. An experimental result of application to five stand tandem cold rolling mill is presented. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=derivative-free%20optimization" title="derivative-free optimization">derivative-free optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=Improving%20Hit%20and%20Run%20method" title=" Improving Hit and Run method"> Improving Hit and Run method</a>, <a href="https://publications.waset.org/abstracts/search?q=real-coded%20genetic%20algorithm" title=" real-coded genetic algorithm"> real-coded genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=rolling%20schedules%20of%20tandem%20cold%20rolling%20mill" title=" rolling schedules of tandem cold rolling mill"> rolling schedules of tandem cold rolling mill</a> </p> <a href="https://publications.waset.org/abstracts/18442/a-hybrid-derivative-free-optimization-method-for-pass-schedule-calculation-in-cold-rolling-mill" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18442.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">696</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">22381</span> A Study on Weight-Reduction of Double Deck High-Speed Train Using Size Optimization Method </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jong-Yeon%20Kim">Jong-Yeon Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Kwang-Bok%20Shin"> Kwang-Bok Shin</a>, <a href="https://publications.waset.org/abstracts/search?q=Tae-Hwan%20Ko"> Tae-Hwan Ko</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The purpose of this paper is to suggest a weight-reduction design method for the aluminum extrusion carbody structure of a double deck high-speed train using size optimization method. The size optimization method was used to optimize thicknesses of skin and rib of the aluminum extrusion for the carbody structure. Thicknesses of 1st underframe, 2nd underframe, solebar and roof frame were selected by design variables in order to conduct size optimization. The results of the size optimization analysis showed that the weight of the aluminum extrusion could be reduced by 0.61 tons (5.60%) compared to the weight of the original carbody structure. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=double%20deck%20high-speed%20train" title="double deck high-speed train">double deck high-speed train</a>, <a href="https://publications.waset.org/abstracts/search?q=size%20optimization" title=" size optimization"> size optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=weigh-reduction" title=" weigh-reduction"> weigh-reduction</a>, <a href="https://publications.waset.org/abstracts/search?q=aluminum%20extrusion" title=" aluminum extrusion"> aluminum extrusion</a> </p> <a href="https://publications.waset.org/abstracts/54728/a-study-on-weight-reduction-of-double-deck-high-speed-train-using-size-optimization-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/54728.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">290</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">22380</span> Ultrasound Assisted Cooling Crystallization of Lactose Monohydrate</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sanjaykumar%20R.%20Patel">Sanjaykumar R. Patel</a>, <a href="https://publications.waset.org/abstracts/search?q=Parth%20R.%20Kayastha"> Parth R. Kayastha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> α-lactose monohydrate is widely used in the pharmaceutical industries as an inactive substance that acts as a vehicle or a medium for a drug or other active substance. It is a byproduct of dairy industries, and the recovery of lactose from whey not only boosts the improvement of the economics of whey utilization but also causes a reduction in pollution as lactose recovery can reduce the BOD of whey by more than 80%. In the present study, levels of process parameters were kept as initial lactose concentration (30-50% w/w), sonication amplitude (20-40%), sonication time (2-6 hours), and crystallization temperature (10-20 <sup>o</sup>C) for the recovery of lactose in ultrasound assisted cooling crystallization. In comparison with cooling crystallization, the use of ultrasound enhanced the lactose recovery by 39.17% (w/w). The parameters were optimized for the lactose recovery using Taguchi Method. The optimum conditions found were initial lactose concentration at level 3 (50% w/w), amplitude of sonication at level 2 (40%), the sonication time at level 3 (6 hours), and crystallization temperature at level 1 (10 °C). The maximum recovery was found to be 85.85% at the optimum conditions. Sonication time and the initial lactose concentration were found to be significant parameters for the lactose recovery. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=crystallization" title="crystallization">crystallization</a>, <a href="https://publications.waset.org/abstracts/search?q=lactose" title=" lactose"> lactose</a>, <a href="https://publications.waset.org/abstracts/search?q=Taguchi%20method" title=" Taguchi method"> Taguchi method</a>, <a href="https://publications.waset.org/abstracts/search?q=ultrasound" title=" ultrasound"> ultrasound</a> </p> <a href="https://publications.waset.org/abstracts/80117/ultrasound-assisted-cooling-crystallization-of-lactose-monohydrate" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/80117.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">212</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">22379</span> A Fuzzy Satisfactory Optimization Method Based on Stress Analysis for a Hybrid Composite Flywheel</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Liping%20Yang">Liping Yang</a>, <a href="https://publications.waset.org/abstracts/search?q=Curran%20Crawford"> Curran Crawford</a>, <a href="https://publications.waset.org/abstracts/search?q=Jr.%20Ren"> Jr. Ren</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhengyi%20Ren"> Zhengyi Ren</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Considering the cost evaluation and the stress analysis, a fuzzy satisfactory optimization (FSO) method has been developed for a hybrid composite flywheel. To evaluate the cost, the cost coefficients of the flywheel components are obtained through calculating the weighted sum of the scores of the material manufacturability, the structure character, and the material price. To express the satisfactory degree of the energy, the cost, and the mass, the satisfactory functions are proposed by using the decline function and introducing a satisfactory coefficient. To imply the different significance of the objectives, the object weight coefficients are defined. Based on the stress analysis of composite material, the circumferential and radial stresses are considered into the optimization formulation. The simulations of the FSO method with different weight coefficients and storage energy density optimization (SEDO) method of a flywheel are contrasted. The analysis results show that the FSO method can satisfy different requirements of the designer and the FSO method with suitable weight coefficients can replace the SEDO method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=flywheel%20energy%20storage" title="flywheel energy storage">flywheel energy storage</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy" title=" fuzzy"> fuzzy</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=stress%20analysis" title=" stress analysis"> stress analysis</a> </p> <a href="https://publications.waset.org/abstracts/72821/a-fuzzy-satisfactory-optimization-method-based-on-stress-analysis-for-a-hybrid-composite-flywheel" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/72821.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">347</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">22378</span> Optimization of Gold Mining Parameters by Cyanidation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Della%20Saddam%20Housseyn">Della Saddam Housseyn</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Gold, the quintessential noble metal, is one of the most popular metals today, given its ever-increasing cost in the international market. The Amesmessa gold deposit is one of the gold-producing deposits. The first step in our job is to analyze the ore (considered rich ore). Mineralogical and chemical analysis has shown that the general constitution of the ore is quartz in addition to other phases such as Al2O3, Fe2O3, CaO, dolomite. The second step consists of all the leaching tests carried out in rolling bottles. These tests were carried out on 14 samples to determine the maximum recovery rate and the optimum consumption of reagent (NaCN and CaO). Tests carried out on a pulp density at 50% solid, 500 ppm cyanide concentration and particle size less than 0.6 mm at alkaline pH gave a recovery rate of 94.37%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cyanide" title="cyanide">cyanide</a>, <a href="https://publications.waset.org/abstracts/search?q=DRX" title=" DRX"> DRX</a>, <a href="https://publications.waset.org/abstracts/search?q=FX" title=" FX"> FX</a>, <a href="https://publications.waset.org/abstracts/search?q=gold" title=" gold"> gold</a>, <a href="https://publications.waset.org/abstracts/search?q=leaching" title=" leaching"> leaching</a>, <a href="https://publications.waset.org/abstracts/search?q=rate%20of%20recovery" title=" rate of recovery"> rate of recovery</a>, <a href="https://publications.waset.org/abstracts/search?q=SAA" title=" SAA"> SAA</a> </p> <a href="https://publications.waset.org/abstracts/142021/optimization-of-gold-mining-parameters-by-cyanidation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/142021.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">181</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">22377</span> Multiobjective Economic Dispatch Using Optimal Weighting Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mandeep%20Kaur">Mandeep Kaur</a>, <a href="https://publications.waset.org/abstracts/search?q=Fatehgarh%20Sahib"> Fatehgarh Sahib</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The purpose of economic load dispatch is to allocate the required load demand between the available generation units such that the cost of operation is minimized. It is an optimization problem to find the most economical schedule of the generating units while satisfying load demand and operational constraints. The multiobjective optimization problem in which the engineer’s goal is to maximize or minimize not a single objective function but several objective functions simultaneously. The purpose of multiobjective problems in the mathematical programming framework is to optimize the different objective functions. Many approaches and methods have been proposed in recent years to solve multiobjective optimization problems. Weighting method has been applied to convert multiobjective optimization problems into scalar optimization. MATLAB 7.10 has been used to write the code for the complete algorithm with the help of genetic algorithm (GA). The validity of the proposed method has been demonstrated on a three-unit power system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=economic%20load%20dispatch" title="economic load dispatch">economic load dispatch</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=generating%20units" title=" generating units"> generating units</a>, <a href="https://publications.waset.org/abstracts/search?q=multiobjective%20optimization" title=" multiobjective optimization"> multiobjective optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=weighting%20method" title=" weighting method"> weighting method</a> </p> <a href="https://publications.waset.org/abstracts/117420/multiobjective-economic-dispatch-using-optimal-weighting-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/117420.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">150</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">22376</span> Measurements of Recovery Stress and Recovery Strain of Ni-Based Shape Memory Alloys </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=W.%20J.%20Kim">W. J. Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The behaviors of the recovery stress and strain of an ultrafine-grained Ni-50.2 at.% Ti alloy prepared by high-ratio differential speed rolling (HRDSR) were examined by a specially designed tensile-testing set up, and the factors that influence the recovery stress and strain were studied. After HRDSR, both the recovery stress and strain were enhanced compared to the initial condition. The constitutive equation showing that the maximum recovery stress is a sole function of the recovery strain was developed based on the experimental data. The recovery strain increased as the yield stress increased. The maximum recovery stress increased with an increase in yield stress. The residual recovery stress was affected by the yield stress as well as the austenite-to-martensite transformation temperature. As the yield stress increased and as the martensitic transformation temperature decreased, the residual recovery stress increased. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=high-ratio%20differential%20speed%20rolling" title="high-ratio differential speed rolling">high-ratio differential speed rolling</a>, <a href="https://publications.waset.org/abstracts/search?q=tensile%20testing" title=" tensile testing"> tensile testing</a>, <a href="https://publications.waset.org/abstracts/search?q=severe%20plastic%20deformation" title=" severe plastic deformation"> severe plastic deformation</a>, <a href="https://publications.waset.org/abstracts/search?q=shape%20memory%20alloys" title=" shape memory alloys"> shape memory alloys</a> </p> <a href="https://publications.waset.org/abstracts/69337/measurements-of-recovery-stress-and-recovery-strain-of-ni-based-shape-memory-alloys" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/69337.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">366</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">22375</span> Blogging Towards Recovery: The Benefits of Blogging about Recovery</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jayme%20R.%20Swanke">Jayme R. Swanke</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study examined the benefits of maintaining public blogs about substance use disorder recovery. The data analyzed for this study included statements about the benefits derived by individuals who blogged about their recovery. The researcher developed classifications of statements that expressed what these individuals gained from blogging into common themes and developed an emerging theory based on these patterns. The findings indicate that these individuals in recovery benefit from blogging by developing connections, processing emotions, remaining accountable, as well as enjoying. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=substance%20use%20disorder%20recovery" title="substance use disorder recovery">substance use disorder recovery</a>, <a href="https://publications.waset.org/abstracts/search?q=connection" title=" connection"> connection</a>, <a href="https://publications.waset.org/abstracts/search?q=blogging" title=" blogging"> blogging</a>, <a href="https://publications.waset.org/abstracts/search?q=accountability" title=" accountability"> accountability</a>, <a href="https://publications.waset.org/abstracts/search?q=processing%20emotions" title=" processing emotions"> processing emotions</a> </p> <a href="https://publications.waset.org/abstracts/143240/blogging-towards-recovery-the-benefits-of-blogging-about-recovery" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/143240.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">180</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">22374</span> Optimization of the Energy Consumption of the Pottery Kilns by the Use of Heat Exchanger as Recovery System and Modeling of Heat Transfer by Conduction Through the Walls of the Furnace</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Maha%20Bakakri">Maha Bakakri</a>, <a href="https://publications.waset.org/abstracts/search?q=Rachid%20Tadili"> Rachid Tadili</a>, <a href="https://publications.waset.org/abstracts/search?q=Fatiha%20Lemmini"> Fatiha Lemmini</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Morocco is one of the few countries that have kept their traditional crafts, despite the competition of modern industry and its impact on manual labor. Therefore the optimization of energy consumption becomes an obligation and this is the purpose of this document. In this work we present some characteristics of the furnace studied, its operating principle and the experimental measurements of the evolutions of the temperatures inside and outside the walls of the furnace, values which will be used later in the calculation of its thermal losses. In order to determine the major source of the thermal losses of the furnace we have established the heat balance of the furnace. The energy consumed, the useful energy and the thermal losses through the walls and the chimney of the furnace are calculated thanks to the experimental measurements which we realized for several firings. The results show that the energy consumption of this type of furnace is very high and that the main source of energy loss is mainly due to the heat losses of the combustion gases that escape from the furnace by the chimney while the losses through the walls are relatively small. it have opted for energy recovery as a solution where we can recover some of the heat lost through the use of a heat exchanger system using a double tube introduced into the flue gas exhaust stack compartment. The study on the heat recovery system is presented and the heat balance inside the exchanger is established. In this paper we also present the numerical modeling of heat transfer by conduction through the walls of the furnace. A numerical model has been established based on the finite volume method and the double scan method. It makes it possible to determine the temperature profile of the furnace and thus to calculate the thermal losses of its walls and to deduce the thermal losses due to the combustion gases. Validation of the model is done using the experimental measurements carried out on the furnace. The results obtained in this work, relating to the energy consumed during the operation of the furnace are important and are part of the energy efficiency framework that has become a key element in global energy policies. It is the fastest and cheapest way to solve energy, environmental and economic security problems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=energy%20cunsumption" title="energy cunsumption">energy cunsumption</a>, <a href="https://publications.waset.org/abstracts/search?q=energy%20recovery" title=" energy recovery"> energy recovery</a>, <a href="https://publications.waset.org/abstracts/search?q=modeling" title=" modeling"> modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=energy%20eficiency" title=" energy eficiency"> energy eficiency</a> </p> <a href="https://publications.waset.org/abstracts/171030/optimization-of-the-energy-consumption-of-the-pottery-kilns-by-the-use-of-heat-exchanger-as-recovery-system-and-modeling-of-heat-transfer-by-conduction-through-the-walls-of-the-furnace" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/171030.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">73</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">22373</span> Improved Particle Swarm Optimization with Cellular Automata and Fuzzy Cellular Automata</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ramin%20Javadzadeh">Ramin Javadzadeh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The particle swarm optimization are Meta heuristic optimization method, which are used for clustering and pattern recognition applications are abundantly. These algorithms in multimodal optimization problems are more efficient than genetic algorithms. A major drawback in these algorithms is their slow convergence to global optimum and their weak stability can be considered in various running of these algorithms. In this paper, improved Particle swarm optimization is introduced for the first time to overcome its problems. The fuzzy cellular automata is used for improving the algorithm efficiently. The credibility of the proposed approach is evaluated by simulations, and it is shown that the proposed approach achieves better results can be achieved compared to the Particle swarm optimization algorithms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cellular%20automata" title="cellular automata">cellular automata</a>, <a href="https://publications.waset.org/abstracts/search?q=cellular%20learning%20automata" title=" cellular learning automata"> cellular learning automata</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20search" title=" local search"> local search</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a> </p> <a href="https://publications.waset.org/abstracts/24739/improved-particle-swarm-optimization-with-cellular-automata-and-fuzzy-cellular-automata" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24739.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">607</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">22372</span> Particle Swarm Optimization and Quantum Particle Swarm Optimization to Multidimensional Function Approximation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Diogo%20Silva">Diogo Silva</a>, <a href="https://publications.waset.org/abstracts/search?q=Fadul%20Rodor"> Fadul Rodor</a>, <a href="https://publications.waset.org/abstracts/search?q=Carlos%20Moraes"> Carlos Moraes</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This work compares the results of multidimensional function approximation using two algorithms: the classical Particle Swarm Optimization (PSO) and the Quantum Particle Swarm Optimization (QPSO). These algorithms were both tested on three functions - The Rosenbrock, the Rastrigin, and the sphere functions - with different characteristics by increasing their number of dimensions. As a result, this study shows that the higher the function space, i.e. the larger the function dimension, the more evident the advantages of using the QPSO method compared to the PSO method in terms of performance and number of necessary iterations to reach the stop criterion. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=PSO" title="PSO">PSO</a>, <a href="https://publications.waset.org/abstracts/search?q=QPSO" title=" QPSO"> QPSO</a>, <a href="https://publications.waset.org/abstracts/search?q=function%20approximation" title=" function approximation"> function approximation</a>, <a href="https://publications.waset.org/abstracts/search?q=AI" title=" AI"> AI</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=multidimensional%20functions" title=" multidimensional functions"> multidimensional functions</a> </p> <a href="https://publications.waset.org/abstracts/81790/particle-swarm-optimization-and-quantum-particle-swarm-optimization-to-multidimensional-function-approximation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/81790.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">589</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">22371</span> A New Family of Globally Convergent Conjugate Gradient Methods</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=B.%20Sellami">B. Sellami</a>, <a href="https://publications.waset.org/abstracts/search?q=Y.%20Laskri"> Y. Laskri</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Belloufi"> M. Belloufi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Conjugate gradient methods are an important class of methods for unconstrained optimization, especially for large-scale problems. Recently, they have been much studied. In this paper, a new family of conjugate gradient method is proposed for unconstrained optimization. This method includes the already existing two practical nonlinear conjugate gradient methods, which produces a descent search direction at every iteration and converges globally provided that the line search satisfies the Wolfe conditions. The numerical experiments are done to test the efficiency of the new method, which implies the new method is promising. In addition the methods related to this family are uniformly discussed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=conjugate%20gradient%20method" title="conjugate gradient method">conjugate gradient method</a>, <a href="https://publications.waset.org/abstracts/search?q=global%20convergence" title=" global convergence"> global convergence</a>, <a href="https://publications.waset.org/abstracts/search?q=line%20search" title=" line search"> line search</a>, <a href="https://publications.waset.org/abstracts/search?q=unconstrained%20optimization" title=" unconstrained optimization"> unconstrained optimization</a> </p> <a href="https://publications.waset.org/abstracts/40381/a-new-family-of-globally-convergent-conjugate-gradient-methods" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/40381.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 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