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particle community algorithm</title> <meta name="description" content="Search results for: modified particle community algorithm"> <meta name="keywords" content="modified particle community algorithm"> <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 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method="get" 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="modified particle community algorithm"> <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> 11642</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: modified particle community algorithm</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">11642</span> Optimal Design of Concrete Shells by Modified Particle Community Algorithm Using Spinless Curves</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Reza%20%20Abbasi">Reza Abbasi</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahmad%20%20Hamidi%20Benam"> Ahmad Hamidi Benam</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Shell structures have many geometrical variables that modify some of these parameters to improve the mechanical behavior of the shell. On the other hand, the behavior of such structures depends on their geometry rather than on mass. Optimization techniques are useful in finding the geometrical shape of shell structures to improve mechanical behavior, especially to prevent or reduce bending anchors. The overall objective of this research is to optimize the shape of concrete shells using the thickness and height parameters along the reference curve and the overall shape of this curve. To implement the proposed scheme, the geometry of the structure was formulated using nonlinear curves. Shell optimization was performed under equivalent static loading conditions using the modified bird community algorithm. The results of this optimization show that without disrupting the initial design and with slight changes in the shell geometry, the structural behavior is significantly improved. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=concrete%20shells" title="concrete shells">concrete shells</a>, <a href="https://publications.waset.org/abstracts/search?q=shape%20optimization" title=" shape optimization"> shape optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=spinless%20curves" title=" spinless curves"> spinless curves</a>, <a href="https://publications.waset.org/abstracts/search?q=modified%20particle%20community%20algorithm" title=" modified particle community algorithm"> modified particle community algorithm</a> </p> <a href="https://publications.waset.org/abstracts/124571/optimal-design-of-concrete-shells-by-modified-particle-community-algorithm-using-spinless-curves" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/124571.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">231</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">11641</span> Modified Active (MA) Algorithm to Generate Semantic Web Related Clustered Hierarchy for Keyword Search</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=G.%20Leena%20Giri">G. Leena Giri</a>, <a href="https://publications.waset.org/abstracts/search?q=Archana%20Mathur"> Archana Mathur</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20H.%20Manjula"> S. H. Manjula</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20R.%20Venugopal"> K. R. Venugopal</a>, <a href="https://publications.waset.org/abstracts/search?q=L.%20M.%20Patnaik"> L. M. Patnaik</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Keyword search in XML documents is based on the notion of lowest common ancestors in the labelled trees model of XML documents and has recently gained a lot of research interest in the database community. In this paper, we propose the Modified Active (MA) algorithm which is an improvement over the active clustering algorithm by taking into consideration the entity aspect of the nodes to find the level of the node pertaining to a particular keyword input by the user. A portion of the bibliography database is used to experimentally evaluate the modified active algorithm and results show that it performs better than the active algorithm. Our modification improves the response time of the system and thereby increases the efficiency of the system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=keyword%20matching%20patterns" title="keyword matching patterns">keyword matching patterns</a>, <a href="https://publications.waset.org/abstracts/search?q=MA%20algorithm" title=" MA algorithm"> MA algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20search" title=" semantic search"> semantic search</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20management" title=" knowledge management"> knowledge management</a> </p> <a href="https://publications.waset.org/abstracts/6608/modified-active-ma-algorithm-to-generate-semantic-web-related-clustered-hierarchy-for-keyword-search" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/6608.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">11640</span> Phasor Measurement Unit Based on Particle Filtering</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rithvik%20Reddy%20Adapa">Rithvik Reddy Adapa</a>, <a href="https://publications.waset.org/abstracts/search?q=Xin%20Wang"> Xin Wang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Phasor Measurement Units (PMUs) are very sophisticated measuring devices that find amplitude, phase and frequency of various voltages and currents in a power system. Particle filter is a state estimation technique that uses Bayesian inference. Particle filters are widely used in pose estimation and indoor navigation and are very reliable. This paper studies and compares four different particle filters as PMUs namely, generic particle filter (GPF), genetic algorithm particle filter (GAPF), particle swarm optimization particle filter (PSOPF) and adaptive particle filter (APF). Two different test signals are used to test the performance of the filters in terms of responsiveness and correctness of the estimates. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=phasor%20measurement%20unit" title="phasor measurement unit">phasor measurement unit</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20filter" title=" particle filter"> particle filter</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=particle%20swarm%20optimisation" title=" particle swarm optimisation"> particle swarm optimisation</a>, <a href="https://publications.waset.org/abstracts/search?q=state%20estimation" title=" state estimation"> state estimation</a> </p> <a href="https://publications.waset.org/abstracts/194127/phasor-measurement-unit-based-on-particle-filtering" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/194127.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">8</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">11639</span> Three-Dimensional Off-Line Path Planning for Unmanned Aerial Vehicle Using Modified Particle Swarm Optimization </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lana%20Dalawr%20Jalal">Lana Dalawr Jalal </a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper addresses the problem of ofﬂine path planning for Unmanned Aerial Vehicles (UAVs) in complex three-dimensional environment with obstacles, which is modelled by 3D Cartesian grid system. Path planning for UAVs require the computational intelligence methods to move aerial vehicles along the flight path effectively to target while avoiding obstacles. In this paper Modified Particle Swarm Optimization (MPSO) algorithm is applied to generate the optimal collision free 3D flight path for UAV. The simulations results clearly demonstrate effectiveness of the proposed algorithm in guiding UAV to the final destination by providing optimal feasible path quickly and effectively. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=obstacle%20avoidance" title="obstacle avoidance">obstacle avoidance</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=three-dimensional%20path%20planning%20unmanned%20aerial%20vehicles" title=" three-dimensional path planning unmanned aerial vehicles"> three-dimensional path planning unmanned aerial vehicles</a> </p> <a href="https://publications.waset.org/abstracts/26160/three-dimensional-off-line-path-planning-for-unmanned-aerial-vehicle-using-modified-particle-swarm-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/26160.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">410</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">11638</span> Particle Filter State Estimation Algorithm Based on Improved Artificial Bee Colony Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Guangyuan%20Zhao">Guangyuan Zhao</a>, <a href="https://publications.waset.org/abstracts/search?q=Nan%20Huang"> Nan Huang</a>, <a href="https://publications.waset.org/abstracts/search?q=Xuesong%20Han"> Xuesong Han</a>, <a href="https://publications.waset.org/abstracts/search?q=Xu%20Huang"> Xu Huang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In order to solve the problem of sample dilution in the traditional particle filter algorithm and achieve accurate state estimation in a nonlinear system, a particle filter method based on an improved artificial bee colony (ABC) algorithm was proposed. The algorithm simulated the process of bee foraging and optimization and made the high likelihood region of the backward probability of particles moving to improve the rationality of particle distribution. The opposition-based learning (OBL) strategy is introduced to optimize the initial population of the artificial bee colony algorithm. The convergence factor is introduced into the neighborhood search strategy to limit the search range and improve the convergence speed. Finally, the crossover and mutation operations of the genetic algorithm are introduced into the search mechanism of the following bee, which makes the algorithm jump out of the local extreme value quickly and continue to search the global extreme value to improve its optimization ability. The simulation results show that the improved method can improve the estimation accuracy of particle filters, ensure the diversity of particles, and improve the rationality of particle distribution. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=particle%20filter" title="particle filter">particle filter</a>, <a href="https://publications.waset.org/abstracts/search?q=impoverishment" title=" impoverishment"> impoverishment</a>, <a href="https://publications.waset.org/abstracts/search?q=state%20estimation" title=" state estimation"> state estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20bee%20colony%20algorithm" title=" artificial bee colony algorithm"> artificial bee colony algorithm</a> </p> <a href="https://publications.waset.org/abstracts/174985/particle-filter-state-estimation-algorithm-based-on-improved-artificial-bee-colony-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/174985.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">151</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">11637</span> A New Optimization Algorithm for Operation of a Microgrid</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sirus%20Mohammadi">Sirus Mohammadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Rohala%20Moghimi"> Rohala Moghimi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The main advantages of microgrids are high energy efficiency through the application of Combined Heat and Power (CHP), high quality and reliability of the delivered electric energy and environmental and economic advantages. This study presents an energy management system (EMS) to optimize the operation of the microgrid (MG). In this paper an Adaptive Modified Firefly Algorithm (AMFA) is presented for optimal operation of a typical MG with renewable energy sources (RESs) accompanied by a back-up Micro-Turbine/Fuel Cell/Battery hybrid power source to level the power mismatch or to store the energy surplus when it’s needed. The problem is formulated as a nonlinear constraint problem to minimize the total operating cost. The management of Energy storage system (ESS), economic load dispatch and operation optimization of distributed generation (DG) are simplified into a single-object optimization problem in the EMS. The proposed algorithm is tested on a typical grid-connected MG including WT/PV/Micro Turbine/Fuel Cell and Energy Storage Devices (ESDs) then its superior performance is compared with those from other evolutionary algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Fuzzy Self Adaptive PSO (FSAPSO), Chaotic Particle PSO (CPSO), Adaptive Modified PSO (AMPSO), and Firefly Algorithm (FA). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=microgrid" title="microgrid">microgrid</a>, <a href="https://publications.waset.org/abstracts/search?q=operation%20management" title=" operation management"> operation management</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=firefly%20algorithm%20%28AMFA%29" title=" firefly algorithm (AMFA)"> firefly algorithm (AMFA)</a> </p> <a href="https://publications.waset.org/abstracts/7163/a-new-optimization-algorithm-for-operation-of-a-microgrid" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/7163.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">341</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">11636</span> Time-Domain Expressions for Bridge Self-Excited Aerodynamic Forces by Modified Particle Swarm Optimizer</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hao-Su%20Liu">Hao-Su Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Jun-Qing%20Lei"> Jun-Qing Lei</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study introduces the theory of modified particle swarm optimizer and its application in time-domain expressions for bridge self-excited aerodynamic forces. Based on the indicial function expression and the rational function expression in time-domain expression for bridge self-excited aerodynamic forces, the characteristics of the two methods, i.e. the modified particle swarm optimizer and conventional search method, are compared in flutter derivatives’ fitting process. Theoretical analysis and numerical results indicate that adopting whether the indicial function expression or the rational function expression, the fitting flutter derivatives obtained by modified particle swarm optimizer have better goodness of fit with ones obtained from experiment. As to the flutter derivatives which have higher nonlinearity, the self-excited aerodynamic forces, using the flutter derivatives obtained through modified particle swarm optimizer fitting process, are much closer to the ones simulated by the experimental. The modified particle swarm optimizer was used to recognize the parameters of time-domain expressions for flutter derivatives of an actual long-span highway-railway truss bridge with double decks at the wind attack angle of 0°, -3° and +3°. It was found that this method could solve the bounded problems of attenuation coefficient effectively in conventional search method, and had the ability of searching in unboundedly area. Accordingly, this study provides a method for engineering industry to frequently and efficiently obtain the time-domain expressions for bridge self-excited aerodynamic forces. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=time-domain%20expressions" title="time-domain expressions">time-domain expressions</a>, <a href="https://publications.waset.org/abstracts/search?q=bridge%20self-excited%20aerodynamic%20forces" title=" bridge self-excited aerodynamic forces"> bridge self-excited aerodynamic forces</a>, <a href="https://publications.waset.org/abstracts/search?q=modified%20particle%20swarm%20optimizer" title=" modified particle swarm optimizer"> modified particle swarm optimizer</a>, <a href="https://publications.waset.org/abstracts/search?q=long-span%20highway-railway%20truss%20bridge" title=" long-span highway-railway truss bridge"> long-span highway-railway truss bridge</a> </p> <a href="https://publications.waset.org/abstracts/69848/time-domain-expressions-for-bridge-self-excited-aerodynamic-forces-by-modified-particle-swarm-optimizer" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/69848.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">314</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">11635</span> Comparison between Continuous Genetic Algorithms and Particle Swarm Optimization for Distribution Network Reconfiguration</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Linh%20Nguyen%20Tung">Linh Nguyen Tung</a>, <a href="https://publications.waset.org/abstracts/search?q=Anh%20Truong%20Viet"> Anh Truong Viet</a>, <a href="https://publications.waset.org/abstracts/search?q=Nghien%20Nguyen%20Ba"> Nghien Nguyen Ba</a>, <a href="https://publications.waset.org/abstracts/search?q=Chuong%20Trinh%20Trong"> Chuong Trinh Trong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper proposes a reconfiguration methodology based on a continuous genetic algorithm (CGA) and particle swarm optimization (PSO) for minimizing active power loss and minimizing voltage deviation. Both algorithms are adapted using graph theory to generate feasible individuals, and the modified crossover is used for continuous variable of CGA. To demonstrate the performance and effectiveness of the proposed methods, a comparative analysis of CGA with PSO for network reconfiguration, on 33-node and 119-bus radial distribution system is presented. The simulation results have shown that both CGA and PSO can be used in the distribution network reconfiguration and CGA outperformed PSO with significant success rate in finding optimal distribution network configuration. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=distribution%20network%20reconfiguration" title="distribution network reconfiguration">distribution network reconfiguration</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=continuous%20genetic%20algorithm" title=" continuous genetic algorithm"> continuous genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=power%20loss%20reduction" title=" power loss reduction"> power loss reduction</a>, <a href="https://publications.waset.org/abstracts/search?q=voltage%20deviation" title=" voltage deviation"> voltage deviation</a> </p> <a href="https://publications.waset.org/abstracts/101407/comparison-between-continuous-genetic-algorithms-and-particle-swarm-optimization-for-distribution-network-reconfiguration" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/101407.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">187</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">11634</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">557</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">11633</span> Optimized Algorithm for Particle Swarm Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fuzhang%20Zhao">Fuzhang Zhao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Particle swarm optimization (PSO) is becoming one of the most important swarm intelligent paradigms for solving global optimization problems. Although some progress has been made to improve PSO algorithms over the last two decades, additional work is still needed to balance parameters to achieve better numerical properties of accuracy, efficiency, and stability. In the optimal PSO algorithm, the optimal weightings of (√ 5 − 1)/2 and (3 − √5)/2 are used for the cognitive factor and the social factor, respectively. By the same token, the same optimal weightings have been applied for intensification searches and diversification searches, respectively. Perturbation and constriction effects are optimally balanced. Simulations of the de Jong, the Rosenbrock, and the Griewank functions show that the optimal PSO algorithm indeed achieves better numerical properties and outperforms the canonical PSO algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=diversification%20search" title="diversification search">diversification search</a>, <a href="https://publications.waset.org/abstracts/search?q=intensification%20search" title=" intensification search"> intensification search</a>, <a href="https://publications.waset.org/abstracts/search?q=optimal%20weighting" title=" optimal weighting"> optimal weighting</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/36390/optimized-algorithm-for-particle-swarm-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/36390.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">11632</span> Numerical Simulation of Unsteady Cases of Fluid Flow Using Modified Dynamic Boundary Condition (mDBC) in Smoothed Particle Hydrodynamics Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Exa%20Heydemans">Exa Heydemans</a>, <a href="https://publications.waset.org/abstracts/search?q=Jessica%20Sjah"> Jessica Sjah</a>, <a href="https://publications.waset.org/abstracts/search?q=Dwinanti%20Rika%20Marthanty"> Dwinanti Rika Marthanty</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents numerical simulations using an open boundary algorithm with modified dynamic boundary condition (mDBC) for weakly compressible smoothed particle hydrodynamics models from particle-based code Dualsphysics. The problems of piping erosion in dams and dikes are aimed for studying the algorithm. The case 2D model of unsteady fluid flow past around a fixed cylinder is simulated, where various values of Reynold’s numbers (Re40, Re60, Re80, and Re100) and different model’s resolution are considered. A constant velocity with different values of viscosity for generating various Reynold’s numbers and different numbers of particles over a cylinder for the resolution are modeled. The interaction between solid particles of the cylinder and fluid particles is concerned. The cylinder is affected by the hydrodynamics force caused by the flow of fluid particles. The solid particles of the cylinder are the observation points to obtain force and pressure due to the hydrodynamics forces. As results of the simulation, which is to show the capability to model 2D unsteady with various Reynold’s numbers, the pressure coefficient, drag coefficient, lift coefficient, and Strouhal number are compared to the previous work from literature. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hydrodynamics" title="hydrodynamics">hydrodynamics</a>, <a href="https://publications.waset.org/abstracts/search?q=internal%20erosion" title=" internal erosion"> internal erosion</a>, <a href="https://publications.waset.org/abstracts/search?q=dualsphysics" title=" dualsphysics"> dualsphysics</a>, <a href="https://publications.waset.org/abstracts/search?q=viscous%20fluid%20flow" title=" viscous fluid flow"> viscous fluid flow</a> </p> <a href="https://publications.waset.org/abstracts/142258/numerical-simulation-of-unsteady-cases-of-fluid-flow-using-modified-dynamic-boundary-condition-mdbc-in-smoothed-particle-hydrodynamics-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/142258.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">165</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">11631</span> An Improved Tracking Approach Using Particle Filter and Background Subtraction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amir%20Mukhtar">Amir Mukhtar</a>, <a href="https://publications.waset.org/abstracts/search?q=Dr.%20Likun%20Xia"> Dr. Likun Xia</a> </p> <p class="card-text"><strong>Abstract:</strong></p> An improved, robust and efficient visual target tracking algorithm using particle filtering is proposed. Particle filtering has been proven very successful in estimating non-Gaussian and non-linear problems. In this paper, the particle filter is used with color feature to estimate the target state with time. Color distributions are applied as this feature is scale and rotational invariant, shows robustness to partial occlusion and computationally efficient. The performance is made more robust by choosing the different (YIQ) color scheme. Tracking is performed by comparison of chrominance histograms of target and candidate positions (particles). Color based particle filter tracking often leads to inaccurate results when light intensity changes during a video stream. Furthermore, background subtraction technique is used for size estimation of the target. The qualitative evaluation of proposed algorithm is performed on several real-world videos. The experimental results demonstrate that the improved algorithm can track the moving objects very well under illumination changes, occlusion and moving background. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=tracking" title="tracking">tracking</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20filter" title=" particle filter"> particle filter</a>, <a href="https://publications.waset.org/abstracts/search?q=histogram" title=" histogram"> histogram</a>, <a href="https://publications.waset.org/abstracts/search?q=corner%20points" title=" corner points"> corner points</a>, <a href="https://publications.waset.org/abstracts/search?q=occlusion" title=" occlusion"> occlusion</a>, <a href="https://publications.waset.org/abstracts/search?q=illumination" title=" illumination"> illumination</a> </p> <a href="https://publications.waset.org/abstracts/3223/an-improved-tracking-approach-using-particle-filter-and-background-subtraction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/3223.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">380</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">11630</span> Pion/Muon Identification in a Nuclear Emulsion Cloud Chamber Using Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kais%20Manai">Kais Manai</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The main part of this work focuses on the study of pion/muon separation at low energy using a nuclear Emulsion Cloud Chamber (ECC) made of lead and nuclear emulsion films. The work consists of two parts: particle reconstruction algorithm and a Neural Network that assigns to each reconstructed particle the probability to be a muon or a pion. The pion/muon separation algorithm has been optimized by using a detailed Monte Carlo simulation of the ECC and tested on real data. The algorithm allows to achieve a 60% muon identification efficiency with a pion misidentification smaller than 3%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=nuclear%20emulsion" title="nuclear emulsion">nuclear emulsion</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20identification" title=" particle identification"> particle identification</a>, <a href="https://publications.waset.org/abstracts/search?q=tracking" title=" tracking"> tracking</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</a> </p> <a href="https://publications.waset.org/abstracts/1437/pionmuon-identification-in-a-nuclear-emulsion-cloud-chamber-using-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/1437.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">506</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">11629</span> Comparison of ANFIS Update Methods Using Genetic Algorithm, Particle Swarm Optimization, and Artificial Bee Colony</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Michael%20R.%20Phangtriastu">Michael R. Phangtriastu</a>, <a href="https://publications.waset.org/abstracts/search?q=Herriyandi%20Herriyandi"> Herriyandi Herriyandi</a>, <a href="https://publications.waset.org/abstracts/search?q=Diaz%20D.%20Santika"> Diaz D. Santika</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a comparison of the implementation of metaheuristic algorithms to train the antecedent parameters and consequence parameters in the adaptive network-based fuzzy inference system (ANFIS). The algorithms compared are genetic algorithm (GA), particle swarm optimization (PSO), and artificial bee colony (ABC). The objective of this paper is to benchmark well-known metaheuristic algorithms. The algorithms are applied to several data set with different nature. The combinations of the algorithms' parameters are tested. In all algorithms, a different number of populations are tested. In PSO, combinations of velocity are tested. In ABC, a different number of limit abandonment are tested. Experiments find out that ABC is more reliable than other algorithms, ABC manages to get better mean square error (MSE) than other algorithms in all data set. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ANFIS" title="ANFIS">ANFIS</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20bee%20colony" title=" artificial bee colony"> artificial bee colony</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=metaheuristic%20algorithm" title=" metaheuristic algorithm"> metaheuristic algorithm</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/68821/comparison-of-anfis-update-methods-using-genetic-algorithm-particle-swarm-optimization-and-artificial-bee-colony" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/68821.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">11628</span> A Hybrid Particle Swarm Optimization-Nelder- Mead Algorithm (PSO-NM) for Nelson-Siegel- Svensson Calibration</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sofia%20Ayouche">Sofia Ayouche</a>, <a href="https://publications.waset.org/abstracts/search?q=Rachid%20Ellaia"> Rachid Ellaia</a>, <a href="https://publications.waset.org/abstracts/search?q=Rajae%20Aboulaich"> Rajae Aboulaich</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Today, insurers may use the yield curve as an indicator evaluation of the profit or the performance of their portfolios; therefore, they modeled it by one class of model that has the ability to fit and forecast the future term structure of interest rates. This class of model is the Nelson-Siegel-Svensson model. Unfortunately, many authors have reported a lot of difficulties when they want to calibrate the model because the optimization problem is not convex and has multiple local optima. In this context, we implement a hybrid Particle Swarm optimization and Nelder Mead algorithm in order to minimize by least squares method, the difference between the zero-coupon curve and the NSS curve. <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=zero-coupon%20curve" title=" zero-coupon curve"> zero-coupon curve</a>, <a href="https://publications.waset.org/abstracts/search?q=Nelson-Siegel-Svensson" title=" Nelson-Siegel-Svensson"> Nelson-Siegel-Svensson</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=Nelder-Mead%20algorithm" title=" Nelder-Mead algorithm"> Nelder-Mead algorithm</a> </p> <a href="https://publications.waset.org/abstracts/48619/a-hybrid-particle-swarm-optimization-nelder-mead-algorithm-pso-nm-for-nelson-siegel-svensson-calibration" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/48619.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">430</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">11627</span> Optimal Cropping Pattern in an Irrigation Project: A Hybrid Model of Artificial Neural Network and Modified Simplex Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Safayat%20Ali%20Shaikh">Safayat Ali Shaikh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Software has been developed for optimal cropping pattern in an irrigation project considering land constraint, water availability constraint and pick up flow constraint using modified Simplex Algorithm. Artificial Neural Network Models (ANN) have been developed to predict rainfall. AR (1) model used to generate 1000 years rainfall data to train the ANN. Simulation has been done with expected rainfall data. Eight number crops and three types of soil class have been considered for optimization model. Area under each crop and each soil class have been quantified using Modified Simplex Algorithm to get optimum net return. Efficacy of the software has been tested using data of large irrigation project in India. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20network" title="artificial neural network">artificial neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=large%20irrigation%20project" title=" large irrigation project"> large irrigation project</a>, <a href="https://publications.waset.org/abstracts/search?q=modified%20simplex%20algorithm" title=" modified simplex algorithm"> modified simplex algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=optimal%20cropping%20pattern" title=" optimal cropping pattern"> optimal cropping pattern</a> </p> <a href="https://publications.waset.org/abstracts/71956/optimal-cropping-pattern-in-an-irrigation-project-a-hybrid-model-of-artificial-neural-network-and-modified-simplex-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/71956.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">203</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">11626</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">552</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">11625</span> Designing State Feedback Multi-Target Controllers by the Use of Particle Swarm Optimization Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Seyedmahdi%20Mousavihashemi">Seyedmahdi Mousavihashemi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> One of the most important subjects of interest in researches is 'improving' which result in various algorithms. In so many geometrical problems we are faced with target functions which should be optimized. In group practices, all the functions’ cooperation lead to convergence. In the study, the optimization algorithm of dense particles is used. Usage of the algorithm improves the given performance norms. The results reveal that usage of swarm algorithm for reinforced particles in designing state feedback improves the given performance norm and in optimized designing of multi-target state feedback controlling, the network will maintain its bearing structure. The results also show that PSO is usable for optimization of state feedback controllers. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multi-objective" title="multi-objective">multi-objective</a>, <a href="https://publications.waset.org/abstracts/search?q=enhanced" title=" enhanced"> enhanced</a>, <a href="https://publications.waset.org/abstracts/search?q=feedback" title=" feedback"> feedback</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=algorithm" title=" algorithm"> algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=particle" title=" particle"> particle</a>, <a href="https://publications.waset.org/abstracts/search?q=design" title=" design"> design</a> </p> <a href="https://publications.waset.org/abstracts/60194/designing-state-feedback-multi-target-controllers-by-the-use-of-particle-swarm-optimization-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/60194.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">499</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">11624</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">11623</span> Minimum Half Power Beam Width and Side Lobe Level Reduction of Linear Antenna Array 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=Saeed%20Ur%20Rahman">Saeed Ur Rahman</a>, <a href="https://publications.waset.org/abstracts/search?q=Naveed%20Ullah"> Naveed Ullah</a>, <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Irshad%20Khan"> Muhammad Irshad Khan</a>, <a href="https://publications.waset.org/abstracts/search?q=Quensheng%20Cao"> Quensheng Cao</a>, <a href="https://publications.waset.org/abstracts/search?q=Niaz%20Muhammad%20Khan"> Niaz Muhammad Khan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper the optimization performance of non-uniform linear antenna array is presented. The Particle Swarm Optimization (PSO) algorithm is presented to minimize Side Lobe Level (SLL) and Half Power Beamwidth (HPBW). The purpose of using the PSO algorithm is to get the optimum values for inter-element spacing and excitation amplitude of linear antenna array that provides a radiation pattern with minimum SLL and HPBW. Various design examples are considered and the obtain results using PSO are confirmed by comparing with results achieved using other nature inspired metaheuristic algorithms such as real coded genetic algorithm (RGA) and biogeography (BBO) algorithm. The comparative results show that optimization of linear antenna array using the PSO provides considerable enhancement in the SLL and HPBW. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=linear%20antenna%20array" title="linear antenna array">linear antenna array</a>, <a href="https://publications.waset.org/abstracts/search?q=minimum%20side%20lobe%20level" title=" minimum side lobe level"> minimum side lobe level</a>, <a href="https://publications.waset.org/abstracts/search?q=narrow%20half%20power%20beamwidth" title=" narrow half power beamwidth"> narrow half power beamwidth</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/76942/minimum-half-power-beam-width-and-side-lobe-level-reduction-of-linear-antenna-array-using-particle-swarm-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/76942.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">552</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">11622</span> Zinc Oxid Nanotubes Modified by SiO2 as a Recyclable Catalyst for the Synthesis of 2,3-Dihydroquinazolin-4(1H)-Ones</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rakhshan%20Hakimelahi">Rakhshan Hakimelahi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In recent years, zinc oxid nano tubes have attracted much attention. The direct use of zinc oxid nano tubes modified by SiO2 as recoverable catalysts for organic reactions is very rare. The catalysts were characterized by XRD. The average particle size of ZnO catalysts is 57 nm and there are high density defects on nano tubes surfaces. A simple and efficient method for the quinazolin derivatives synthesis from the condensation isatoic anhydride and an aromatic aldehyde with ammonium acetate in the presence of a catalytic amount zinc oxid nano tubes modified by SiO2 is described. The reason proposed for higher catalytic activity of zinc oxid nano tubes modified by SiO2 is a combination effect of the small particle size and high-density surface defects. The practical and simple method led to excellent yields of the 2,3-Di hydro quinazolin-4(1H)-one derivatives under mild conditions and within short times. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=2" title="2">2</a>, <a href="https://publications.waset.org/abstracts/search?q=3-Dihydroquinazolin-4%281H%29-one%20derivatives" title="3-Dihydroquinazolin-4(1H)-one derivatives">3-Dihydroquinazolin-4(1H)-one derivatives</a>, <a href="https://publications.waset.org/abstracts/search?q=reusable%20catalyst" title=" reusable catalyst"> reusable catalyst</a>, <a href="https://publications.waset.org/abstracts/search?q=SiO2" title=" SiO2"> SiO2</a>, <a href="https://publications.waset.org/abstracts/search?q=zinc%20oxid%20nanotubes" title=" zinc oxid nanotubes"> zinc oxid nanotubes</a> </p> <a href="https://publications.waset.org/abstracts/57045/zinc-oxid-nanotubes-modified-by-sio2-as-a-recyclable-catalyst-for-the-synthesis-of-23-dihydroquinazolin-41h-ones" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/57045.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">372</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">11621</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">11620</span> A Multidimensional Genetic Algorithm Applicable for Our VRP Variant Dealing with the Problems of Infrastructure Defaults SVRDP-CMTW: “Safety Vehicle Routing Diagnosis Problem with Control and Modified Time Windows”</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ben%20Mansour%20Mouin">Ben Mansour Mouin</a>, <a href="https://publications.waset.org/abstracts/search?q=Elloumi%20Abdelkarim"> Elloumi Abdelkarim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We will discuss the problem of routing a fleet of different vehicles from a central depot to different types of infrastructure-defaults with dynamic maintenance requests, modified time windows, and control of default maintained. For this reason, we propose a modified metaheuristicto to solve our mathematical model. SVRDP-CMTW is a variant VRP of an optimal vehicle plan that facilitates the maintenance task of different types of infrastructure-defaults. This task will be monitored after the maintenance, based on its priorities, the degree of danger associated with each default, and the neighborhood at the black-spots. We will present, in this paper, a multidimensional genetic algorithm “MGA” by detailing its characteristics, proposed mechanisms, and roles in our work. The coding of this algorithm represents the necessary parameters that characterize each infrastructure-default with the objective of minimizing a combination of cost, distance and maintenance times while satisfying the priority levels of the most urgent defaults. The developed algorithm will allow the dynamic integration of newly detected defaults at the execution time. This result will be displayed in our programmed interactive system at the routing time. This multidimensional genetic algorithm replaces N genetic algorithm to solve P different type problems of infrastructure defaults (instead of N algorithm for P problem we can solve in one multidimensional algorithm simultaneously who can solve all these problemsatonce). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mathematical%20model" title="mathematical model">mathematical model</a>, <a href="https://publications.waset.org/abstracts/search?q=VRP" title=" VRP"> VRP</a>, <a href="https://publications.waset.org/abstracts/search?q=multidimensional%20genetic%20algorithm" title=" multidimensional genetic algorithm"> multidimensional genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=metaheuristics" title=" metaheuristics"> metaheuristics</a> </p> <a href="https://publications.waset.org/abstracts/141979/a-multidimensional-genetic-algorithm-applicable-for-our-vrp-variant-dealing-with-the-problems-of-infrastructure-defaults-svrdp-cmtw-safety-vehicle-routing-diagnosis-problem-with-control-and-modified-time-windows" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/141979.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">196</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">11619</span> Refactoring Object Oriented Software through Community Detection Using Evolutionary Computation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=R.%20Nagarani">R. Nagarani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> An intrinsic property of software in a real-world environment is its need to evolve, which is usually accompanied by the increase of software complexity and deterioration of software quality, making software maintenance a tough problem. Refactoring is regarded as an effective way to address this problem. Many refactoring approaches at the method and class level have been proposed. But the extent of research on software refactoring at the package level is less. This work presents a novel approach to refactor the package structures of object oriented software using genetic algorithm based community detection. It uses software networks to represent classes and their dependencies. It uses a constrained community detection algorithm to obtain the optimized community structures in software networks, which also correspond to the optimized package structures. It finally provides a list of classes as refactoring candidates by comparing the optimized package structures with the real package structures. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=community%20detection" title="community detection">community detection</a>, <a href="https://publications.waset.org/abstracts/search?q=complex%20network" title=" complex network"> complex network</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=package" title=" package"> package</a>, <a href="https://publications.waset.org/abstracts/search?q=refactoring" title=" refactoring"> refactoring</a> </p> <a href="https://publications.waset.org/abstracts/31191/refactoring-object-oriented-software-through-community-detection-using-evolutionary-computation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31191.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">418</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">11618</span> A Comparison of Sequential Quadratic Programming, Genetic Algorithm, Simulated Annealing, Particle Swarm Optimization for the Design and Optimization of a Beam Column</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nima%20Khosravi">Nima Khosravi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper describes an integrated optimization technique with concurrent use of sequential quadratic programming, genetic algorithm, and simulated annealing particle swarm optimization for the design and optimization of a beam column. In this research, the comparison between 4 different types of optimization methods. The comparison is done and it is found out that all the methods meet the required constraints and the lowest value of the objective function is achieved by SQP, which was also the fastest optimizer to produce the results. SQP is a gradient based optimizer hence its results are usually the same after every run. The only thing which affects the results is the initial conditions given. The initial conditions given in the various test run were very large as compared. Hence, the value converged at a different point. Rest of the methods is a heuristic method which provides different values for different runs even if every parameter is kept constant. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=beam%20column" title="beam column">beam column</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=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=sequential%20quadratic%20programming" title=" sequential quadratic programming"> sequential quadratic programming</a>, <a href="https://publications.waset.org/abstracts/search?q=simulated%20annealing" title=" simulated annealing"> simulated annealing</a> </p> <a href="https://publications.waset.org/abstracts/58973/a-comparison-of-sequential-quadratic-programming-genetic-algorithm-simulated-annealing-particle-swarm-optimization-for-the-design-and-optimization-of-a-beam-column" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/58973.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">386</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">11617</span> Collocation Method Using Quartic B-Splines for Solving the Modified RLW Equation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20A.%20Soliman">A. A. Soliman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Modified Regularized Long Wave (MRLW) equation is solved numerically by giving a new algorithm based on collocation method using quartic B-splines at the mid-knot points as element shape. Also, we use the fourth Runge-Kutta method for solving the system of first order ordinary differential equations instead of finite difference method. Our test problems, including the migration and interaction of solitary waves, are used to validate the algorithm which is found to be accurate and efficient. The three invariants of the motion are evaluated to determine the conservation properties of the algorithm. The temporal evaluation of a Maxwellian initial pulse is then studied. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=collocation%20method" title="collocation method">collocation method</a>, <a href="https://publications.waset.org/abstracts/search?q=MRLW%20equation" title=" MRLW equation"> MRLW equation</a>, <a href="https://publications.waset.org/abstracts/search?q=Quartic%20B-splines" title=" Quartic B-splines"> Quartic B-splines</a>, <a href="https://publications.waset.org/abstracts/search?q=solitons" title=" solitons"> solitons</a> </p> <a href="https://publications.waset.org/abstracts/7664/collocation-method-using-quartic-b-splines-for-solving-the-modified-rlw-equation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/7664.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">303</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">11616</span> Global Convergence of a Modified Three-Term Conjugate Gradient Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Belloufi%20Mohammed">Belloufi Mohammed</a>, <a href="https://publications.waset.org/abstracts/search?q=Sellami%20Badreddine"> Sellami Badreddine</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper deals with a new nonlinear modified three-term conjugate gradient algorithm for solving large-scale unstrained optimization problems. The search direction of the algorithms from this class has three terms and is computed as modifications of the classical conjugate gradient algorithms to satisfy both the descent and the conjugacy conditions. An example of three-term conjugate gradient algorithm from this class, as modifications of the classical and well known Hestenes and Stiefel or of the CG_DESCENT by Hager and Zhang conjugate gradient algorithms, satisfying both the descent and the conjugacy conditions is presented. Under mild conditions, we prove that the modified three-term conjugate gradient algorithm with Wolfe type line search is globally convergent. Preliminary numerical results show the proposed method is very promising. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=unconstrained%20optimization" title="unconstrained optimization">unconstrained optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=three-term%20conjugate%20gradient" title=" three-term conjugate gradient"> three-term conjugate gradient</a>, <a href="https://publications.waset.org/abstracts/search?q=sufficient%20descent%20property" title=" sufficient descent property"> sufficient descent property</a>, <a href="https://publications.waset.org/abstracts/search?q=line%20search" title=" line search"> line search</a> </p> <a href="https://publications.waset.org/abstracts/41727/global-convergence-of-a-modified-three-term-conjugate-gradient-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/41727.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">375</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">11615</span> Improvement Image Summarization using Image Processing and Particle swarm optimization Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hooman%20Torabifard">Hooman Torabifard</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the last few years, with the progress of technology and computers and artificial intelligence entry into all kinds of scientific and industrial fields, the lifestyles of human life have changed and in general, the way of humans live on earth has many changes and development. Until now, some of the changes has occurred in the context of digital images and image processing and still continues. However, besides all the benefits, there have been disadvantages. One of these disadvantages is the multiplicity of images with high volume and data; the focus of this paper is on improving and developing a method for summarizing and enhancing the productivity of these images. The general method used for this purpose in this paper consists of a set of methods based on data obtained from image processing and using the PSO (Particle swarm optimization) algorithm. In the remainder of this paper, the method used is elaborated in detail. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20summarization" title="image summarization">image summarization</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=image%20threshold" title=" image threshold"> image threshold</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20processing" title=" image processing"> image processing</a> </p> <a href="https://publications.waset.org/abstracts/138289/improvement-image-summarization-using-image-processing-and-particle-swarm-optimization-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/138289.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">133</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">11614</span> A Novel Algorithm for Production Scheduling</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ali%20Mohammadi%20Bolban%20Abad">Ali Mohammadi Bolban Abad</a>, <a href="https://publications.waset.org/abstracts/search?q=Fariborz%20Ahmadi"> Fariborz Ahmadi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Optimization in manufacture is a method to use limited resources to obtain the best performance and reduce waste. In this paper a new algorithm based on eurygaster life is introduced to obtain a plane in which task order and completion time of resources are defined. Evaluation results show our approach has less make span when the resources are allocated with some products in comparison to genetic algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=evolutionary%20computation" title="evolutionary computation">evolutionary computation</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=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=NP-Hard%20problems" title=" NP-Hard problems"> NP-Hard problems</a>, <a href="https://publications.waset.org/abstracts/search?q=production%20scheduling" title=" production scheduling"> production scheduling</a> </p> <a href="https://publications.waset.org/abstracts/23914/a-novel-algorithm-for-production-scheduling" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/23914.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">378</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">11613</span> Influence of Modified and Unmodified Cow Bone on the Mechanical Properties of Reinforced Polyester Composites for Biomedical Applications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=I.%20O.%20Oladele">I. O. Oladele</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20A.%20Omotoyinbo"> J. A. Omotoyinbo</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20M.%20Okoro"> A. M. Okoro</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20G.%20Okikiola"> A. G. Okikiola</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20L.%20Olajide"> J. L. Olajide</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This work was carried out to investigate comparatively the effects of modified and unmodified cow bone particles on the mechanical properties of polyester matrix composites in order to investigate the suitability of the materials as biomaterial. Cow bones were procured from an abattoir, sun dried for 4 weeks and crushed. The crushed bones were divided into two, where one part was turned to ash while the other part was pulverized with laboratory ball mill before the two grades were sieved using 75 µm sieve size. Bone ash and bone particle reinforced tensile and flexural composite samples were developed from pre-determined proportions of 2, 4, 6, and 8 %. The samples after curing were stripped from the moulds and were allowed to further cure for 3 weeks before tensile and flexural tests were performed on them. The tensile test result showed that, 8 wt % bone particle reinforced polyester composites has higher tensile properties except for modulus of elasticity where 8 wt % bone ash particle reinforced composites has higher value while for flexural test, bone ash particle reinforced composites demonstrate the best flexural properties. The results show that these materials are structurally compatible. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=biomedical" title="biomedical">biomedical</a>, <a href="https://publications.waset.org/abstracts/search?q=composites" title=" composites"> composites</a>, <a href="https://publications.waset.org/abstracts/search?q=cow%20bone" title=" cow bone"> cow bone</a>, <a href="https://publications.waset.org/abstracts/search?q=mechanical%20properties" title=" mechanical properties"> mechanical properties</a>, <a href="https://publications.waset.org/abstracts/search?q=polyester" title=" polyester"> polyester</a>, <a href="https://publications.waset.org/abstracts/search?q=reinforcement" title=" reinforcement"> reinforcement</a> </p> <a href="https://publications.waset.org/abstracts/47045/influence-of-modified-and-unmodified-cow-bone-on-the-mechanical-properties-of-reinforced-polyester-composites-for-biomedical-applications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/47045.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">279</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=modified%20particle%20community%20algorithm&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=modified%20particle%20community%20algorithm&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=modified%20particle%20community%20algorithm&page=4">4</a></li> <li class="page-item"><a class="page-link" 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