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optimization</title> <meta name="description" content="Search results for: field optimization"> <meta name="keywords" content="field optimization"> <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 class="d-block d-lg-none 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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="field optimization"> <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> 11253</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: field optimization</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">11253</span> SFO-ECRSEP: Sensor Field Optim谋zation Based Ecrsep For Heterogeneous WSNS</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gagandeep%20Singh">Gagandeep Singh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The sensor field optimization is a serious issue in WSNs and has been ignored by many researchers. As in numerous real-time sensing fields the sensor nodes on the corners i.e. on the segment boundaries will become lifeless early because no extraordinary safety is presented for them. Accordingly, in this research work the central objective is on the segment based optimization by separating the sensor field between advance and normal segments. The inspiration at the back this sensor field optimization is to extend the time spam when the first sensor node dies. For the reason that in normal sensor nodes which were exist on the borders may become lifeless early because the space among them and the base station is more so they consume more power so at last will become lifeless soon. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=WSNs" title="WSNs">WSNs</a>, <a href="https://publications.waset.org/abstracts/search?q=ECRSEP" title=" ECRSEP"> ECRSEP</a>, <a href="https://publications.waset.org/abstracts/search?q=SEP" title=" SEP"> SEP</a>, <a href="https://publications.waset.org/abstracts/search?q=field%20optimization" title=" field optimization"> field optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=energy" title=" energy"> energy</a> </p> <a href="https://publications.waset.org/abstracts/15452/sfo-ecrsep-sensor-field-optimization-based-ecrsep-for-heterogeneous-wsns" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15452.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">300</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">11252</span> Design Optimization of a Compact Quadrupole Electromagnet for CLS 2.0</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Md.%20Armin%20Islam">Md. Armin Islam</a>, <a href="https://publications.waset.org/abstracts/search?q=Les%20Dallin"> Les Dallin</a>, <a href="https://publications.waset.org/abstracts/search?q=Mark%20Boland"> Mark Boland</a>, <a href="https://publications.waset.org/abstracts/search?q=W.%20J.%20Zhang"> W. J. Zhang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper reports a study on the optimal magnetic design of a compact quadrupole electromagnet for the Canadian Light Source (CLS 2.0). The nature of the design is to determine a quadrupole with low relative higher order harmonics and better field quality. The design problem was formulated as an optimization model, in which the objective function is the higher order harmonics (multipole errors) and the variable to be optimized is the material distribution on the pole. The higher order harmonics arose in the quadrupole due to truncating the ideal hyperbola at a certain point to make the pole. In this project, the arisen harmonics have been optimized both transversely and longitudinally by adjusting material on the poles in a controlled way. For optimization, finite element analysis (FEA) has been conducted. A better higher order harmonics amplitudes and field quality have been achieved through the optimization. On the basis of the optimized magnetic design, electrical and cooling calculation has been performed for the magnet. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=drift" title="drift">drift</a>, <a href="https://publications.waset.org/abstracts/search?q=electrical" title=" electrical"> electrical</a>, <a href="https://publications.waset.org/abstracts/search?q=and%20cooling%20calculation" title=" and cooling calculation"> and cooling calculation</a>, <a href="https://publications.waset.org/abstracts/search?q=integrated%20field" title=" integrated field"> integrated field</a>, <a href="https://publications.waset.org/abstracts/search?q=magnetic%20field%20gradient" title=" magnetic field gradient"> magnetic field gradient</a>, <a href="https://publications.waset.org/abstracts/search?q=multipole%20errors" title=" multipole errors"> multipole errors</a>, <a href="https://publications.waset.org/abstracts/search?q=quadrupole" title=" quadrupole"> quadrupole</a> </p> <a href="https://publications.waset.org/abstracts/106773/design-optimization-of-a-compact-quadrupole-electromagnet-for-cls-20" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/106773.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">143</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">11251</span> Optimization Analysis of a Concentric Tube Heat Exchanger with Field Synergy Principle</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20C.%20Lin">M. C. Lin</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20W.%20Su"> C. W. Su</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The paper investigates the optimization analysis to the heat exchanger design, mainly with response surface method and genetic algorithm to explore the relationship between optimal fluid flow velocity and temperature of the heat exchanger using field synergy principle. First, finite volume method is proposed to calculate the flow temperature and flow rate distribution for numerical analysis. We identify the most suitable simulation equations by response surface methodology. Furthermore, a genetic algorithm approach is applied to optimize the relationship between fluid flow velocity and flow temperature of the heat exchanger. The results show that the field synergy angle plays vital role in the performance of a true heat exchanger. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=optimization%20analysis" title="optimization analysis">optimization analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=field%20synergy" title=" field synergy"> field synergy</a>, <a href="https://publications.waset.org/abstracts/search?q=heat%20exchanger" title=" heat exchanger"> heat exchanger</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a> </p> <a href="https://publications.waset.org/abstracts/50449/optimization-analysis-of-a-concentric-tube-heat-exchanger-with-field-synergy-principle" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/50449.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">307</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">11250</span> Reinforcement Learning Optimization: Unraveling Trends and Advancements in Metaheuristic Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rahul%20Paul">Rahul Paul</a>, <a href="https://publications.waset.org/abstracts/search?q=Kedar%20Nath%20Das"> Kedar Nath Das</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The field of machine learning (ML) is experiencing rapid development, resulting in a multitude of theoretical advancements and extensive practical implementations across various disciplines. The objective of ML is to facilitate the ability of machines to perform cognitive tasks by leveraging knowledge gained from prior experiences and effectively addressing complex problems, even in situations that deviate from previously encountered instances. Reinforcement Learning (RL) has emerged as a prominent subfield within ML and has gained considerable attention in recent times from researchers. This surge in interest can be attributed to the practical applications of RL, the increasing availability of data, and the rapid advancements in computing power. At the same time, optimization algorithms play a pivotal role in the field of ML and have attracted considerable interest from researchers. A multitude of proposals have been put forth to address optimization problems or improve optimization techniques within the domain of ML. The necessity of a thorough examination and implementation of optimization algorithms within the context of ML is of utmost importance in order to provide guidance for the advancement of research in both optimization and ML. This article provides a comprehensive overview of the application of metaheuristic evolutionary optimization algorithms in conjunction with RL to address a diverse range of scientific challenges. Furthermore, this article delves into the various challenges and unresolved issues pertaining to the optimization of RL models. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title="machine learning">machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=reinforcement%20learning" title=" reinforcement learning"> reinforcement learning</a>, <a href="https://publications.waset.org/abstracts/search?q=loss%20function" title=" loss function"> loss function</a>, <a href="https://publications.waset.org/abstracts/search?q=evolutionary%20optimization%20techniques" title=" evolutionary optimization techniques"> evolutionary optimization techniques</a> </p> <a href="https://publications.waset.org/abstracts/170676/reinforcement-learning-optimization-unraveling-trends-and-advancements-in-metaheuristic-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/170676.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">75</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">11249</span> Global Optimization Techniques for Optimal Placement of HF Antennas on a Shipboard</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mustafa%20Ural">Mustafa Ural</a>, <a href="https://publications.waset.org/abstracts/search?q=Can%20Bayseferogulari"> Can Bayseferogulari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this work, radio frequency (RF) coupling between two HF antennas on a shipboard platform is minimized by determining an optimal antenna placement. Unlike the other works, the coupling is minimized not only at single frequency but over the whole frequency band of operation. Similarly, GAO and PSO, are used in order to determine optimal antenna placement. Throughout this work, outputs of two optimization techniques are compared with each other in terms of antenna placements and coupling results. At the end of the work, far-field radiation pattern performances of the antennas at their optimal places are analyzed in terms of directivity and coverage in order to see that. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electromagnetic%20compatibility" title="electromagnetic compatibility">electromagnetic compatibility</a>, <a href="https://publications.waset.org/abstracts/search?q=antenna%20placement" title=" antenna placement"> antenna placement</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm%20optimization" title=" genetic algorithm optimization"> genetic algorithm 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/108667/global-optimization-techniques-for-optimal-placement-of-hf-antennas-on-a-shipboard" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/108667.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">236</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">11248</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">11247</span> Design and Optimization of Flow Field for Cavitation Reduction of Valve Sleeves </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kamal%20Upadhyay">Kamal Upadhyay</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhou%20Hua"> Zhou Hua</a>, <a href="https://publications.waset.org/abstracts/search?q=Yu%20Rui"> Yu Rui</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper aims to improve the streamline linked with the flow field and cavitation on the valve sleeve. We observed that local pressure fluctuation produces a low-pressure zone, central to the formation of vapor volume fraction within the valve chamber led to air-bubbles (or cavities). Thus, it allows simultaneously to a severe negative impact on the inner surface and lifespan of the valve sleeves. Cavitation reduction is a vitally important issue to pressure control valves. The optimization of the flow field is proposed in this paper to reduce the cavitation of valve sleeves. In this method, the inner wall of the valve sleeve is changed from a cylindrical surface to the conical surface, leading to the decline of the fluid flow velocity and the rise of the outlet pressure. Besides, the streamline is distributed inside the sleeve uniformly. Thus, the bubble generation is lessened. The fluid models are built and analysis of flow field distribution, pressure, vapor volume and velocity was carried out using computational fluid dynamics (CFD) and numerical technique. The results indicate that this structure can suppress the cavitation of valve sleeves effectively. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=streamline" title="streamline">streamline</a>, <a href="https://publications.waset.org/abstracts/search?q=cavitation" title=" cavitation"> cavitation</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=computational%20fluid%20dynamics" title=" computational fluid dynamics"> computational fluid dynamics</a> </p> <a href="https://publications.waset.org/abstracts/107922/design-and-optimization-of-flow-field-for-cavitation-reduction-of-valve-sleeves" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/107922.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">145</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">11246</span> The Application of Artificial Neural Network for Bridge Structures Design Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Angga%20S.%20Fajar">Angga S. Fajar</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Aminullah"> A. Aminullah</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20Kiyono"> J. Kiyono</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20A.%20Safitri"> R. A. Safitri</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper discusses about the application of ANN for optimizing of bridge structure design. ANN has been applied in various field of science concerning prediction and optimization. The structural optimization has several benefit including accelerate structural design process, saving the structural material, and minimize self-weight and mass of structure. In this paper, there are three types of bridge structure that being optimized including PSC I-girder superstructure, composite steel-concrete girder superstructure, and RC bridge pier. The different optimization strategy on each bridge structure implement back propagation method of ANN is conducted in this research. The optimal weight and easier design process of bridge structure with satisfied error are achieved. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bridge%20structures" title="bridge structures">bridge structures</a>, <a href="https://publications.waset.org/abstracts/search?q=ANN" title=" ANN"> ANN</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=back%20propagation" title=" back propagation"> back propagation</a> </p> <a href="https://publications.waset.org/abstracts/58189/the-application-of-artificial-neural-network-for-bridge-structures-design-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/58189.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">373</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">11245</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">11244</span> Power Circuit Schemes in AC Drive is Made by Condition of the Minimum Electric Losses</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20A.%20Grigoryev">M. A. Grigoryev</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20N.%20Shishkov"> A. N. Shishkov</a>, <a href="https://publications.waset.org/abstracts/search?q=D.%20A.%20Sychev"> D. A. Sychev</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The article defines the necessity of choosing the optimal power circuits scheme of the electric drive with field regulated reluctance machine. The specific weighting factors are calculation, the linear regression dependence of specific losses in semiconductor frequency converters are presented depending on the values of the rated current. It is revealed that with increase of the carrier frequency PWM improves the output current waveform, but increases the loss, so you will need depending on the task in a certain way to choose from the carrier frequency. For task of optimization by criterion of the minimum electrical losses regression dependence of the electrical losses in the frequency converter circuit at a frequency of a PWM signal of 0 Hz. The surface optimization criterion is presented depending on the rated output torque of the motor and number of phases. In electric drives with field regulated reluctance machine with at low output power optimization criterion appears to be the worst for multiphase circuits. With increasing output power this trend hold true, but becomes insignificantly different optimal solutions for three-phase and multiphase circuits. This is explained to the linearity of the dependence of the electrical losses from the current. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=field%20regulated%20reluctance%20machine" title="field regulated reluctance machine">field regulated reluctance machine</a>, <a href="https://publications.waset.org/abstracts/search?q=the%20electrical%20losses" title=" the electrical losses"> the electrical losses</a>, <a href="https://publications.waset.org/abstracts/search?q=multiphase%20power%20circuit" title=" multiphase power circuit"> multiphase power circuit</a>, <a href="https://publications.waset.org/abstracts/search?q=the%20surface%20optimization%20criterion" title=" the surface optimization criterion"> the surface optimization criterion</a> </p> <a href="https://publications.waset.org/abstracts/46140/power-circuit-schemes-in-ac-drive-is-made-by-condition-of-the-minimum-electric-losses" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/46140.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">295</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">11243</span> Optimization and Simulation Models Applied in Engineering Planning and Management</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abiodun%20Ladanu%20Ajala">Abiodun Ladanu Ajala</a>, <a href="https://publications.waset.org/abstracts/search?q=Wuyi%20Oke"> Wuyi Oke</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Mathematical simulation and optimization models packaged within interactive computer programs provide a common way for planners and managers to predict the behaviour of any proposed water resources system design or management policy before it is implemented. Modeling presents a principal technique of predicting the behaviour of the proposed infrastructural designs or management policies. Models can be developed and used to help identify specific alternative plans that best meet those objectives. This study discusses various types of models, their development, architecture, data requirements, and applications in the field of engineering. It also outlines the advantages and limitations of each the optimization and simulation models presented. The techniques explored in this review include; dynamic programming, linear programming, fuzzy optimization, evolutionary algorithms and finally artificial intelligence techniques. Previous studies carried out using some of the techniques mentioned above were reviewed, and most of the results from different researches showed that indeed optimization and simulation provides viable alternatives and predictions which form a basis for decision making in building engineering structures and also in engineering planning and management. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=linear%20programming" title="linear programming">linear programming</a>, <a href="https://publications.waset.org/abstracts/search?q=mutation" title=" mutation"> mutation</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=simulation" title=" simulation"> simulation</a> </p> <a href="https://publications.waset.org/abstracts/67525/optimization-and-simulation-models-applied-in-engineering-planning-and-management" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/67525.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">590</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">11242</span> A Mean鈥揤ariance鈥揝kewness Portfolio Optimization Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kostas%20Metaxiotis">Kostas Metaxiotis</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Portfolio optimization is one of the most important topics in finance. This paper proposes a mean–variance–skewness (MVS) portfolio optimization model. Traditionally, the portfolio optimization problem is solved by using the mean–variance (MV) framework. In this study, we formulate the proposed model as a three-objective optimization problem, where the portfolio's expected return and skewness are maximized whereas the portfolio risk is minimized. For solving the proposed three-objective portfolio optimization model we apply an adapted version of the non-dominated sorting genetic algorithm (NSGAII). Finally, we use a real dataset from FTSE-100 for validating the proposed model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=evolutionary%20algorithms" title="evolutionary algorithms">evolutionary algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=portfolio%20optimization" title=" portfolio optimization"> portfolio optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=skewness" title=" skewness"> skewness</a>, <a href="https://publications.waset.org/abstracts/search?q=stock%20selection" title=" stock selection"> stock selection</a> </p> <a href="https://publications.waset.org/abstracts/102472/a-mean-variance-skewness-portfolio-optimization-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/102472.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">198</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">11241</span> Improved Whale Algorithm Based on Information Entropy and Its Application in Truss Structure Optimization Design</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Serges%20Mendomo%20%20Meye">Serges Mendomo Meye</a>, <a href="https://publications.waset.org/abstracts/search?q=Li%20Guowei"> Li Guowei</a>, <a href="https://publications.waset.org/abstracts/search?q=Shen%20Zhenzhong"> Shen Zhenzhong</a>, <a href="https://publications.waset.org/abstracts/search?q=Gan%20Lei"> Gan Lei</a>, <a href="https://publications.waset.org/abstracts/search?q=Xu%20Liqun"> Xu Liqun</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Given the limitations of the original whale optimization algorithm (WAO) in local optimum and low convergence accuracy in truss structure optimization problems, based on the fundamental whale algorithm, an improved whale optimization algorithm (SWAO) based on information entropy is proposed. The information entropy itself is an uncertain measure. It is used to control the range of whale searches in path selection. It can overcome the shortcomings of the basic whale optimization algorithm (WAO) and can improve the global convergence speed of the algorithm. Taking truss structure as the optimization research object, the mathematical model of truss structure optimization is established; the cross-sectional area of truss is taken as the design variable; the objective function is the weight of truss structure; and an improved whale optimization algorithm (SWAO) is used for optimization design, which provides a new idea and means for its application in large and complex engineering structure optimization design. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=information%20entropy" title="information entropy">information entropy</a>, <a href="https://publications.waset.org/abstracts/search?q=structural%20optimization" title=" structural optimization"> structural optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=truss%20structure" title=" truss structure"> truss structure</a>, <a href="https://publications.waset.org/abstracts/search?q=whale%20algorithm" title=" whale algorithm"> whale algorithm</a> </p> <a href="https://publications.waset.org/abstracts/139986/improved-whale-algorithm-based-on-information-entropy-and-its-application-in-truss-structure-optimization-design" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/139986.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">249</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">11240</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">11239</span> Non-Stationary Stochastic Optimization of an Oscillating Water Column</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mar%C3%ADa%20L.%20Jal%C3%B3n">Mar铆a L. Jal贸n</a>, <a href="https://publications.waset.org/abstracts/search?q=Feargal%20Brennan"> Feargal Brennan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A non-stationary stochastic optimization methodology is applied to an OWC (oscillating water column) to find the design that maximizes the wave energy extraction. Different temporal cycles are considered to represent the long-term variability of the wave climate at the site in the optimization problem. The results of the non-stationary stochastic optimization problem are compared against those obtained by a stationary stochastic optimization problem. The comparative analysis reveals that the proposed non-stationary optimization provides designs with a better fit to reality. However, the stationarity assumption can be adequate when looking at averaged system response. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=non-stationary%20stochastic%20optimization" title="non-stationary stochastic optimization">non-stationary stochastic optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=oscillating%20water" title=" oscillating water"> oscillating water</a>, <a href="https://publications.waset.org/abstracts/search?q=temporal%20variability" title=" temporal variability"> temporal variability</a>, <a href="https://publications.waset.org/abstracts/search?q=wave%20energy" title=" wave energy"> wave energy</a> </p> <a href="https://publications.waset.org/abstracts/75300/non-stationary-stochastic-optimization-of-an-oscillating-water-column" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/75300.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">373</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">11238</span> Two-Stage Approach for Solving the Multi-Objective Optimization Problem on Combinatorial Configurations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Liudmyla%20Koliechkina">Liudmyla Koliechkina</a>, <a href="https://publications.waset.org/abstracts/search?q=Olena%20Dvirna"> Olena Dvirna</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The statement of the multi-objective optimization problem on combinatorial configurations is formulated, and the approach to its solution is proposed. The problem is of interest as a combinatorial optimization one with many criteria, which is a model of many applied tasks. The approach to solving the multi-objective optimization problem on combinatorial configurations consists of two stages; the first is the reduction of the multi-objective problem to the single criterion based on existing multi-objective optimization methods, the second stage solves the directly replaced single criterion combinatorial optimization problem by the horizontal combinatorial method. This approach provides the optimal solution to the multi-objective optimization problem on combinatorial configurations, taking into account additional restrictions for a finite number of steps. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=discrete%20set" title="discrete set">discrete set</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20combinatorial%20optimization" title=" linear combinatorial optimization"> linear combinatorial optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-objective%20optimization" title=" multi-objective optimization"> multi-objective optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=Pareto%20solutions" title=" Pareto solutions"> Pareto solutions</a>, <a href="https://publications.waset.org/abstracts/search?q=partial%20permutation%20set" title=" partial permutation set"> partial permutation set</a>, <a href="https://publications.waset.org/abstracts/search?q=structural%20graph" title=" structural graph"> structural graph</a> </p> <a href="https://publications.waset.org/abstracts/133824/two-stage-approach-for-solving-the-multi-objective-optimization-problem-on-combinatorial-configurations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/133824.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">167</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">11237</span> Co-Evolutionary Fruit Fly Optimization Algorithm and Firefly Algorithm for Solving Unconstrained Optimization Problems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=R.%20M.%20Rizk-Allah">R. M. Rizk-Allah</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents co-evolutionary fruit fly optimization algorithm based on firefly algorithm (CFOA-FA) for solving unconstrained optimization problems. The proposed algorithm integrates the merits of fruit fly optimization algorithm (FOA), firefly algorithm (FA) and elite strategy to refine the performance of classical FOA. Moreover, co-evolutionary mechanism is performed by applying FA procedures to ensure the diversity of the swarm. Finally, the proposed algorithm CFOA- FA is tested on several benchmark problems from the usual literature and the numerical results have demonstrated the superiority of the proposed algorithm for finding the global optimal solution. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=firefly%20algorithm" title="firefly algorithm">firefly algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=fruit%20fly%20optimization%20algorithm" title=" fruit fly optimization algorithm"> fruit fly optimization algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=unconstrained%20optimization%20problems" title=" unconstrained optimization problems"> unconstrained optimization problems</a> </p> <a href="https://publications.waset.org/abstracts/15923/co-evolutionary-fruit-fly-optimization-algorithm-and-firefly-algorithm-for-solving-unconstrained-optimization-problems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15923.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">536</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">11236</span> Model of Optimal Centroids Approach for Multivariate Data Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pham%20Van%20Nha">Pham Van Nha</a>, <a href="https://publications.waset.org/abstracts/search?q=Le%20Cam%20Binh"> Le Cam Binh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Particle swarm optimization (PSO) is a population-based stochastic optimization algorithm. PSO was inspired by the natural behavior of birds and fish in migration and foraging for food. PSO is considered as a multidisciplinary optimization model that can be applied in various optimization problems. PSO’s ideas are simple and easy to understand but PSO is only applied in simple model problems. We think that in order to expand the applicability of PSO in complex problems, PSO should be described more explicitly in the form of a mathematical model. In this paper, we represent PSO in a mathematical model and apply in the multivariate data classification. First, PSOs general mathematical model (MPSO) is analyzed as a universal optimization model. Then, Model of Optimal Centroids (MOC) is proposed for the multivariate data classification. Experiments were conducted on some benchmark data sets to prove the effectiveness of MOC compared with several proposed schemes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=analysis%20of%20optimization" title="analysis of optimization">analysis of optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence%20based%20optimization" title=" artificial intelligence based optimization"> artificial intelligence based optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization%20for%20learning%20and%20data%20analysis" title=" optimization for learning and data analysis"> optimization for learning and data analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=global%20optimization" title=" global optimization"> global optimization</a> </p> <a href="https://publications.waset.org/abstracts/126058/model-of-optimal-centroids-approach-for-multivariate-data-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/126058.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">208</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">11235</span> Reinforcement Learning for Quality-Oriented Production Process Parameter Optimization Based on Predictive Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Akshay%20Paranjape">Akshay Paranjape</a>, <a href="https://publications.waset.org/abstracts/search?q=Nils%20Plettenberg"> Nils Plettenberg</a>, <a href="https://publications.waset.org/abstracts/search?q=Robert%20Schmitt"> Robert Schmitt</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Producing faulty products can be costly for manufacturing companies and wastes resources. To reduce scrap rates in manufacturing, process parameters can be optimized using machine learning. Thus far, research mainly focused on optimizing specific processes using traditional algorithms. To develop a framework that enables real-time optimization based on a predictive model for an arbitrary production process, this study explores the application of reinforcement learning (RL) in this field. Based on a thorough review of literature about RL and process parameter optimization, a model based on maximum a posteriori policy optimization that can handle both numerical and categorical parameters is proposed. A case study compares the model to state鈥搊f鈥搕he鈥揳rt traditional algorithms and shows that RL can find optima of similar quality while requiring significantly less time. These results are confirmed in a large-scale validation study on data sets from both production and other fields. Finally, multiple ways to improve the model are discussed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=reinforcement%20learning" title="reinforcement learning">reinforcement learning</a>, <a href="https://publications.waset.org/abstracts/search?q=production%20process%20optimization" title=" production process optimization"> production process optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=evolutionary%20algorithms" title=" evolutionary algorithms"> evolutionary algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=policy%20optimization" title=" policy optimization"> policy optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=actor%20critic%20approach" title=" actor critic approach"> actor critic approach</a> </p> <a href="https://publications.waset.org/abstracts/160123/reinforcement-learning-for-quality-oriented-production-process-parameter-optimization-based-on-predictive-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/160123.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">97</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">11234</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"> G眉lay 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">11233</span> Geospatial Network Analysis Using Particle Swarm Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Varun%20Singh">Varun Singh</a>, <a href="https://publications.waset.org/abstracts/search?q=Mainak%20Bandyopadhyay"> Mainak Bandyopadhyay</a>, <a href="https://publications.waset.org/abstracts/search?q=Maharana%20Pratap%20Singh"> Maharana Pratap Singh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The shortest path (SP) problem concerns with finding the shortest path from a specific origin to a specified destination in a given network while minimizing the total cost associated with the path. This problem has widespread applications. Important applications of the SP problem include vehicle routing in transportation systems particularly in the field of in-vehicle Route Guidance System (RGS) and traffic assignment problem (in transportation planning). Well known applications of evolutionary methods like Genetic Algorithms (GA), Ant Colony Optimization, Particle Swarm Optimization (PSO) have come up to solve complex optimization problems to overcome the shortcomings of existing shortest path analysis methods. It has been reported by various researchers that PSO performs better than other evolutionary optimization algorithms in terms of success rate and solution quality. Further Geographic Information Systems (GIS) have emerged as key information systems for geospatial data analysis and visualization. This research paper is focused towards the application of PSO for solving the shortest path problem between multiple points of interest (POI) based on spatial data of Allahabad City and traffic speed data collected using GPS. Geovisualization of results of analysis is carried out in GIS. <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=GIS" title=" GIS"> GIS</a>, <a href="https://publications.waset.org/abstracts/search?q=traffic%20data" title=" traffic data"> traffic data</a>, <a href="https://publications.waset.org/abstracts/search?q=outliers" title=" outliers"> outliers</a> </p> <a href="https://publications.waset.org/abstracts/13181/geospatial-network-analysis-using-particle-swarm-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13181.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">483</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">11232</span> Conservativeness of Probabilistic Constrained Optimal Control Method for Unknown Probability Distribution</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tomoaki%20Hashimoto">Tomoaki Hashimoto</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In recent decades, probabilistic constrained optimal control problems have attracted much attention in many research field. Although probabilistic constraints are generally intractable in an optimization problem, several tractable methods haven been proposed to handle probabilistic constraints. In most methods, probabilistic constraints are reduced to deterministic constraints that are tractable in an optimization problem. However, there is a gap between the transformed deterministic constraints in case of known and unknown probability distribution. This paper examines the conservativeness of probabilistic constrained optimization method with the unknown probability distribution. The objective of this paper is to provide a quantitative assessment of the conservatism for tractable constraints in probabilistic constrained optimization with the unknown probability distribution. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=optimal%20control" title="optimal control">optimal control</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20systems" title=" stochastic systems"> stochastic systems</a>, <a href="https://publications.waset.org/abstracts/search?q=discrete%20time%20systems" title=" discrete time systems"> discrete time systems</a>, <a href="https://publications.waset.org/abstracts/search?q=probabilistic%20constraints" title=" probabilistic constraints"> probabilistic constraints</a> </p> <a href="https://publications.waset.org/abstracts/35585/conservativeness-of-probabilistic-constrained-optimal-control-method-for-unknown-probability-distribution" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/35585.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">581</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">11231</span> An Algorithm of Set-Based Particle Swarm Optimization with Status Memory for Traveling Salesman Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Takahiro%20Hino">Takahiro Hino</a>, <a href="https://publications.waset.org/abstracts/search?q=Michiharu%20Maeda"> Michiharu Maeda</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Particle swarm optimization (PSO) is an optimization approach that achieves the social model of bird flocking and fish schooling. PSO works in continuous space and can solve continuous optimization problem with high quality. Set-based particle swarm optimization (SPSO) functions in discrete space by using a set. SPSO can solve combinatorial optimization problem with high quality and is successful to apply to the large-scale problem. In this paper, we present an algorithm of SPSO with status memory to decide the position based on the previous position for solving traveling salesman problem (TSP). In order to show the effectiveness of our approach. We examine SPSOSM for TSP compared to the existing algorithms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=combinatorial%20optimization%20problems" title="combinatorial optimization problems">combinatorial optimization problems</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=set-based%20particle%20swarm%20optimization" title=" set-based particle swarm optimization"> set-based particle swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=traveling%20salesman%20problem" title=" traveling salesman problem"> traveling salesman problem</a> </p> <a href="https://publications.waset.org/abstracts/47282/an-algorithm-of-set-based-particle-swarm-optimization-with-status-memory-for-traveling-salesman-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/47282.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">553</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">11230</span> Optimization of Proton Exchange Membrane Fuel Cell Parameters Based on Modified Particle Swarm Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Dezvarei">M. Dezvarei</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Morovati"> S. Morovati</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In recent years, increasing usage of electrical energy provides a widespread field for investigating new methods to produce clean electricity with high reliability and cost management. Fuel cells are new clean generations to make electricity and thermal energy together with high performance and no environmental pollution. According to the expansion of fuel cell usage in different industrial networks, the identification and optimization of its parameters is really significant. This paper presents optimization of a proton exchange membrane fuel cell (PEMFC) parameters based on modified particle swarm optimization with real valued mutation (RVM) and clonal algorithms. Mathematical equations of this type of fuel cell are presented as the main model structure in the optimization process. Optimized parameters based on clonal and RVM algorithms are compared with the desired values in the presence and absence of measurement noise. This paper shows that these methods can improve the performance of traditional optimization methods. Simulation results are employed to analyze and compare the performance of these methodologies in order to optimize the proton exchange membrane fuel cell parameters. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=clonal%20algorithm" title="clonal algorithm">clonal algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=proton%20exchange%20membrane%20fuel%20cell%20%28PEMFC%29" title=" proton exchange membrane fuel cell (PEMFC)"> proton exchange membrane fuel cell (PEMFC)</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization%20%28PSO%29" title=" particle swarm optimization (PSO)"> particle swarm optimization (PSO)</a>, <a href="https://publications.waset.org/abstracts/search?q=real-valued%20mutation%20%28RVM%29" title=" real-valued mutation (RVM)"> real-valued mutation (RVM)</a> </p> <a href="https://publications.waset.org/abstracts/51618/optimization-of-proton-exchange-membrane-fuel-cell-parameters-based-on-modified-particle-swarm-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/51618.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">351</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">11229</span> Optimization of Technical and Technological Solutions for the Development of Offshore Hydrocarbon Fields in the Kaliningrad Region</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pavel%20Shcherban">Pavel Shcherban</a>, <a href="https://publications.waset.org/abstracts/search?q=Viktoria%20Ivanova"> Viktoria Ivanova</a>, <a href="https://publications.waset.org/abstracts/search?q=Alexander%20Neprokin"> Alexander Neprokin</a>, <a href="https://publications.waset.org/abstracts/search?q=Vladislav%20Golovanov"> Vladislav Golovanov</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Currently, LLC «Lukoil-Kaliningradmorneft» is implementing a comprehensive program for the development of offshore fields of the Kaliningrad region. This is largely associated with the depletion of the resource base of land in the region, as well as the positive results of geological investigation surrounding the Baltic Sea area and the data on the volume of hydrocarbon recovery from a single offshore field are working on the Kaliningrad region – D-6 «Kravtsovskoye».The article analyzes the main stages of the LLC «Lukoil-Kaliningradmorneft»’s development program for the development of the hydrocarbon resources of the region's shelf and suggests an optimization algorithm that allows managing a multi-criteria process of development of shelf deposits. The algorithm is formed on the basis of the problem of sequential decision making, which is a section of dynamic programming. Application of the algorithm during the consolidation of the initial data, the elaboration of project documentation, the further exploration and development of offshore fields will allow to optimize the complex of technical and technological solutions and increase the economic efficiency of the field development project implemented by LLC «Lukoil-Kaliningradmorneft». <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=offshore%20fields%20of%20hydrocarbons%20of%20the%20Baltic%20Sea" title="offshore fields of hydrocarbons of the Baltic Sea">offshore fields of hydrocarbons of the Baltic Sea</a>, <a href="https://publications.waset.org/abstracts/search?q=development%20of%20offshore%20oil%20and%20gas%20fields" title=" development of offshore oil and gas fields"> development of offshore oil and gas fields</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization%20of%20the%20field%20development%20scheme" title=" optimization of the field development scheme"> optimization of the field development scheme</a>, <a href="https://publications.waset.org/abstracts/search?q=solution%20of%20multicriteria%20tasks%20in%20oil%20and%20gas%20complex" title=" solution of multicriteria tasks in oil and gas complex"> solution of multicriteria tasks in oil and gas complex</a>, <a href="https://publications.waset.org/abstracts/search?q=quality%20management%20in%20oil%20and%20gas%20complex" title=" quality management in oil and gas complex"> quality management in oil and gas complex</a> </p> <a href="https://publications.waset.org/abstracts/95275/optimization-of-technical-and-technological-solutions-for-the-development-of-offshore-hydrocarbon-fields-in-the-kaliningrad-region" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/95275.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">200</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">11228</span> Application of the Global Optimization Techniques to the Optical Thin Film Design</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=D.%20Li">D. Li</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Optical thin films are used in a wide variety of optical components and there are many software tools programmed for advancing multilayer thin film design. The available software packages for designing the thin film structure may not provide optimum designs. Normally, almost all current software programs obtain their final designs either from optimizing a starting guess or by technique, which may or may not involve a pseudorandom process, that give different answers every time, depending upon the initial conditions. With the increasing power of personal computers, functional methods in optimization and synthesis of optical multilayer systems have been developed such as DGL Optimization, Simulated Annealing, Genetic Algorithms, Needle Optimization, Inductive Optimization and Flip-Flop Optimization. Among these, DGL Optimization has proved its efficiency in optical thin film designs. The application of the DGL optimization technique to the design of optical coating is presented. A DGL optimization technique is provided, and its main features are discussed. Guidelines on the application of the DGL optimization technique to various types of design problems are given. The innovative global optimization strategies used in a software tool, OnlyFilm, to optimize multilayer thin film designs through different filter designs are outlined. OnlyFilm is a powerful, versatile, and user-friendly thin film software on the market, which combines optimization and synthesis design capabilities with powerful analytical tools for optical thin film designers. It is also the only thin film design software that offers a true global optimization function. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=optical%20coatings" title="optical coatings">optical coatings</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=design%20software" title=" design software"> design software</a>, <a href="https://publications.waset.org/abstracts/search?q=thin%20film%20design" title=" thin film design"> thin film design</a> </p> <a href="https://publications.waset.org/abstracts/80917/application-of-the-global-optimization-techniques-to-the-optical-thin-film-design" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/80917.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">316</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">11227</span> Optimization of Interface Radio of Universal Mobile Telecommunication System Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=O.%20Mohamed%20Amine">O. Mohamed Amine</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Khireddine"> A. Khireddine</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Telecoms operators are always looking to meet their share of the other customers, they try to gain optimum utilization of the deployed equipment and network optimization has become essential. This project consists of optimizing UMTS network, and the study area is an urban area situated in the center of Algiers. It was initially questions to become familiar with the different communication systems (3G) and the optimization technique, its main components, and its fundamental characteristics radios were introduced. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=UMTS" title="UMTS">UMTS</a>, <a href="https://publications.waset.org/abstracts/search?q=UTRAN" title=" UTRAN"> UTRAN</a>, <a href="https://publications.waset.org/abstracts/search?q=WCDMA" title=" WCDMA"> WCDMA</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a> </p> <a href="https://publications.waset.org/abstracts/52556/optimization-of-interface-radio-of-universal-mobile-telecommunication-system-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/52556.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">384</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">11226</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">11225</span> Applications of Artificial Neural Networks in Civil Engineering </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Naci%20B%C3%BCy%C3%BCkkarac%C4%B1%C4%9Fan">Naci B眉y眉kkarac谋臒an</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Artificial neural networks (ANN) is an electrical model based on the human brain nervous system and working principle. Artificial neural networks have been the subject of an active field of research that has matured greatly over the past 55 years. ANN now is used in many fields. But, it has been viewed that artificial neural networks give better results in particular optimization and control systems. There are requirements of optimization and control system in many of the area forming the subject of civil engineering applications. In this study, the first artificial intelligence systems are widely used in the solution of civil engineering systems were examined with the basic principles and technical aspects. Finally, the literature reviews for applications in the field of civil engineering were conducted and also artificial intelligence techniques were informed about the study and its results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20networks" title="artificial neural networks">artificial neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=civil%20engineering" title=" civil engineering"> civil engineering</a>, <a href="https://publications.waset.org/abstracts/search?q=Fuzzy%20logic" title=" Fuzzy logic"> Fuzzy logic</a>, <a href="https://publications.waset.org/abstracts/search?q=statistics" title=" statistics"> statistics</a> </p> <a href="https://publications.waset.org/abstracts/29908/applications-of-artificial-neural-networks-in-civil-engineering" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/29908.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">412</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">11224</span> Topology Optimization of Composite Structures with Material Nonlinearity</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mengxiao%20Li">Mengxiao Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Johnson%20Zhang"> Johnson Zhang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Currently, topology optimization technique is widely used to define the layout design of structures that are presented as truss-like topologies. However, due to the difficulty in combining optimization technique with more realistic material models where their nonlinear properties should be considered, the achieved optimized topologies are commonly unable to apply straight towards the practical design problems. This study presented an optimization procedure of composite structures where different elastic stiffness, yield criteria, and hardening models are assumed for the candidate materials. From the results, it can be concluded that a more explicit modeling has the significant influence on the resulting topologies. Also, the isotropic or kinematic hardening is important for elastoplastic structural optimization design. The capability of the proposed optimization procedure is shown through several cases. <p class="card-text"><strong>Keywords:</strong> <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=material%20composition" title=" material composition"> material composition</a>, <a href="https://publications.waset.org/abstracts/search?q=nonlinear%20%20modeling" title=" nonlinear modeling"> nonlinear modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=hardening%20rules" title=" hardening rules"> hardening rules</a> </p> <a href="https://publications.waset.org/abstracts/63520/topology-optimization-of-composite-structures-with-material-nonlinearity" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/63520.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info 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