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swarm optimisation</title> <meta name="description" content="Search results for: particle swarm optimisation"> <meta name="keywords" content="particle swarm optimisation"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="particle swarm optimisation"> <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> 1904</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: particle swarm optimisation</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1904</span> Robust Optimisation Model and Simulation-Particle Swarm Optimisation Approach for Vehicle Routing Problem with Stochastic Demands</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohanad%20Al-Behadili">Mohanad Al-Behadili</a>, <a href="https://publications.waset.org/abstracts/search?q=Djamila%20Ouelhadj"> Djamila Ouelhadj</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a specific type of vehicle routing problem under stochastic demand (SVRP) is considered. This problem is of great importance because it models for many of the real world vehicle routing applications. This paper used a robust optimisation model to solve the problem along with the novel Simulation-Particle Swarm Optimisation (Sim-PSO) approach. The proposed Sim-PSO approach is based on the hybridization of the Monte Carlo simulation technique with the PSO algorithm. A comparative study between the proposed model and the Sim-PSO approach against other solution methods in the literature has been given in this paper. This comparison including the Analysis of Variance (ANOVA) to show the ability of the model and solution method in solving the complicated SVRP. The experimental results show that the proposed model and Sim-PSO approach has a significant impact on the obtained solution by providing better quality solutions comparing with well-known algorithms in the literature. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=stochastic%20vehicle%20routing%20problem" title="stochastic vehicle routing problem">stochastic vehicle routing problem</a>, <a href="https://publications.waset.org/abstracts/search?q=robust%20optimisation%20model" title=" robust optimisation model"> robust optimisation model</a>, <a href="https://publications.waset.org/abstracts/search?q=Monte%20Carlo%20simulation" title=" Monte Carlo simulation"> Monte Carlo simulation</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimisation" title=" particle swarm optimisation"> particle swarm optimisation</a> </p> <a href="https://publications.waset.org/abstracts/86908/robust-optimisation-model-and-simulation-particle-swarm-optimisation-approach-for-vehicle-routing-problem-with-stochastic-demands" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/86908.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">277</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">1903</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">1902</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">1901</span> The Utilization of Particle Swarm Optimization Method to Solve Nurse Scheduling Problem </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Norhayati%20Mohd%20Rasip">Norhayati Mohd Rasip</a>, <a href="https://publications.waset.org/abstracts/search?q=Abd.%20Samad%20Hasan%20Basari"> Abd. Samad Hasan Basari </a>, <a href="https://publications.waset.org/abstracts/search?q=Nuzulha%20Khilwani%20Ibrahim"> Nuzulha Khilwani Ibrahim</a>, <a href="https://publications.waset.org/abstracts/search?q=Burairah%20Hussin"> Burairah Hussin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The allocation of working schedule especially for shift environment is hard to fulfill its fairness among them. In the case of nurse scheduling, to set up the working time table for them is time consuming and complicated, which consider many factors including rules, regulation and human factor. The scenario is more complicated since most nurses are women which have personnel constraints and maternity leave factors. The undesirable schedule can affect the nurse productivity, social life and the absenteeism can significantly as well affect patient's life. This paper aimed to enhance the scheduling process by utilizing the particle swarm optimization in order to solve nurse scheduling problem. The result shows that the generated multiple initial schedule is fulfilled the requirements and produces the lowest cost of constraint violation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=nurse%20scheduling" title="nurse scheduling">nurse scheduling</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=nurse%20rostering" title=" nurse rostering"> nurse rostering</a>, <a href="https://publications.waset.org/abstracts/search?q=hard%20and%20soft%20constraint" title=" hard and soft constraint"> hard and soft constraint</a> </p> <a href="https://publications.waset.org/abstracts/30425/the-utilization-of-particle-swarm-optimization-method-to-solve-nurse-scheduling-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/30425.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">1900</span> Particle Swarm Optimization and Quantum Particle Swarm Optimization to Multidimensional Function Approximation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Diogo%20Silva">Diogo Silva</a>, <a href="https://publications.waset.org/abstracts/search?q=Fadul%20Rodor"> Fadul Rodor</a>, <a href="https://publications.waset.org/abstracts/search?q=Carlos%20Moraes"> Carlos Moraes</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This work compares the results of multidimensional function approximation using two algorithms: the classical Particle Swarm Optimization (PSO) and the Quantum Particle Swarm Optimization (QPSO). These algorithms were both tested on three functions - The Rosenbrock, the Rastrigin, and the sphere functions - with different characteristics by increasing their number of dimensions. As a result, this study shows that the higher the function space, i.e. the larger the function dimension, the more evident the advantages of using the QPSO method compared to the PSO method in terms of performance and number of necessary iterations to reach the stop criterion. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=PSO" title="PSO">PSO</a>, <a href="https://publications.waset.org/abstracts/search?q=QPSO" title=" QPSO"> QPSO</a>, <a href="https://publications.waset.org/abstracts/search?q=function%20approximation" title=" function approximation"> function approximation</a>, <a href="https://publications.waset.org/abstracts/search?q=AI" title=" AI"> AI</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=multidimensional%20functions" title=" multidimensional functions"> multidimensional functions</a> </p> <a href="https://publications.waset.org/abstracts/81790/particle-swarm-optimization-and-quantum-particle-swarm-optimization-to-multidimensional-function-approximation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/81790.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">589</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1899</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">1898</span> Printed Thai Character Recognition Using 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=Phawin%20Sangsuvan">Phawin Sangsuvan</a>, <a href="https://publications.waset.org/abstracts/search?q=Chutimet%20Srinilta"> Chutimet Srinilta</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This Paper presents the applications of Particle Swarm Optimization (PSO) Method for Thai optical character recognition (OCR). OCR consists of the pre-processing, character recognition and post-processing. Before enter into recognition process. The Character must be “Prepped” by pre-processing process. The PSO is an optimization method that belongs to the swarm intelligence family based on the imitation of social behavior patterns of animals. Route of each particle is determined by an individual data among neighborhood particles. The interaction of the particles with neighbors is the advantage of Particle Swarm to determine the best solution. So PSO is interested by a lot of researchers in many difficult problems including character recognition. As the previous this research used a Projection Histogram to extract printed digits features and defined the simple Fitness Function for PSO. The results reveal that PSO gives 67.73% for testing dataset. So in the future there can be explored enhancement the better performance of PSO with improve the Fitness Function. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=character%20recognition" title="character recognition">character recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=histogram%20projection" title=" histogram projection"> histogram projection</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=pattern%20recognition%20techniques" title=" pattern recognition techniques "> pattern recognition techniques </a> </p> <a href="https://publications.waset.org/abstracts/25613/printed-thai-character-recognition-using-particle-swarm-optimization-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/25613.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">477</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">1897</span> Particle Swarm Optimisation of a Terminal Synergetic Controllers for a DC-DC Converter</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=H.%20Abderrezek">H. Abderrezek</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20N.%20Harmas"> M. N. Harmas</a> </p> <p class="card-text"><strong>Abstract:</strong></p> DC-DC converters are widely used as reliable power source for many industrial and military applications, computers and electronic devices. Several control methods were developed for DC-DC converters control mostly with asymptotic convergence. Synergetic control (SC) is a proven robust control approach and will be used here in a so-called terminal scheme to achieve finite time convergence. Lyapunov synthesis is adopted to assure controlled system stability. Furthermore particle swarm optimization (PSO) algorithm, based on an integral time absolute of error (ITAE) criterion will be used to optimize controller parameters. Simulation of terminal synergetic control of a DC-DC converter is carried out for different operating conditions and results are compared to classic synergetic control performance, that which demonstrate the effectiveness and feasibility of the proposed control method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=DC-DC%20converter" title="DC-DC converter">DC-DC converter</a>, <a href="https://publications.waset.org/abstracts/search?q=PSO" title=" PSO"> PSO</a>, <a href="https://publications.waset.org/abstracts/search?q=finite%20time" title=" finite time"> finite time</a>, <a href="https://publications.waset.org/abstracts/search?q=terminal" title=" terminal"> terminal</a>, <a href="https://publications.waset.org/abstracts/search?q=synergetic%20control" title=" synergetic control"> synergetic control</a> </p> <a href="https://publications.waset.org/abstracts/12644/particle-swarm-optimisation-of-a-terminal-synergetic-controllers-for-a-dc-dc-converter" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/12644.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">502</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">1896</span> Demand Forecasting Using Artificial Neural Networks Optimized by Particle Swarm Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Daham%20Owaid%20Matrood">Daham Owaid Matrood</a>, <a href="https://publications.waset.org/abstracts/search?q=Naqaa%20Hussein%20Raheem"> Naqaa Hussein Raheem</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Evolutionary algorithms and Artificial neural networks (ANN) are two relatively young research areas that were subject to a steadily growing interest during the past years. This paper examines the use of Particle Swarm Optimization (PSO) to train a multi-layer feed forward neural network for demand forecasting. We use in this paper weekly demand data for packed cement and towels, which have been outfitted by the Northern General Company for Cement and General Company of prepared clothes respectively. The results showed superiority of trained neural networks using particle swarm optimization on neural networks trained using error back propagation because their ability to escape from local optima. <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=demand%20forecasting" title=" demand forecasting"> demand forecasting</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=weight%20optimization" title=" weight optimization"> weight optimization</a> </p> <a href="https://publications.waset.org/abstracts/45069/demand-forecasting-using-artificial-neural-networks-optimized-by-particle-swarm-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/45069.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">452</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">1895</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">1894</span> Optimization of Cloud Classification Using Particle Swarm Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Riffi%20Mohammed%20Amine">Riffi Mohammed Amine</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A cloud is made up of small particles of liquid water or ice suspended in the atmosphere, which generally do not reach the ground. Various methods are used to classify clouds. This article focuses specifically on a technique known as particle swarm optimization (PSO), an AI approach inspired by the collective behaviors of animals living in groups, such as schools of fish and flocks of birds, and a method used to solve complex classification and optimization problems with approximate solutions. The proposed technique was evaluated using a series of second-generation METOSAT images taken by the MSG satellite. The acquired results indicate that the proposed method gave acceptable results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=remote%20sensing" title="remote sensing">remote sensing</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=clouds" title=" clouds"> clouds</a>, <a href="https://publications.waset.org/abstracts/search?q=meteorological%20image" title=" meteorological image"> meteorological image</a> </p> <a href="https://publications.waset.org/abstracts/192148/optimization-of-cloud-classification-using-particle-swarm-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/192148.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">15</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1893</span> [Keynote]: No-Trust-Zone Architecture for Securing Supervisory Control and Data Acquisition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Michael%20Okeke">Michael Okeke</a>, <a href="https://publications.waset.org/abstracts/search?q=Andrew%20Blyth"> Andrew Blyth</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Supervisory Control And Data Acquisition (SCADA) as the state of the art Industrial Control Systems (ICS) are used in many different critical infrastructures, from smart home to energy systems and from locomotives train system to planes. Security of SCADA systems is vital since many lives depend on it for daily activities and deviation from normal operation could be disastrous to the environment as well as lives. This paper describes how No-Trust-Zone (NTZ) architecture could be incorporated into SCADA Systems in order to reduce the chances of malicious intent. The architecture is made up of two distinctive parts which are; the field devices such as; sensors, PLCs pumps, and actuators. The second part of the architecture is designed following lambda architecture, which is made up of a detection algorithm based on Particle Swarm Optimization (PSO) and Hadoop framework for data processing and storage. Apache Spark will be a part of the lambda architecture for real-time analysis of packets for anomalies detection. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=industrial%20control%20system%20%28ics" title="industrial control system (ics">industrial control system (ics</a>, <a href="https://publications.waset.org/abstracts/search?q=no-trust-zone%20%28ntz%29" title=" no-trust-zone (ntz)"> no-trust-zone (ntz)</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimisation%20%28pso%29" title=" particle swarm optimisation (pso)"> particle swarm optimisation (pso)</a>, <a href="https://publications.waset.org/abstracts/search?q=supervisory%20control%20and%20data%20acquisition%20%28scada%29" title=" supervisory control and data acquisition (scada)"> supervisory control and data acquisition (scada)</a>, <a href="https://publications.waset.org/abstracts/search?q=swarm%20intelligence%20%28SI%29" title=" swarm intelligence (SI)"> swarm intelligence (SI)</a> </p> <a href="https://publications.waset.org/abstracts/53994/keynote-no-trust-zone-architecture-for-securing-supervisory-control-and-data-acquisition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/53994.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">345</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">1892</span> An Enhanced Particle Swarm Optimization Algorithm for Multiobjective Problems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Houda%20Abadlia">Houda Abadlia</a>, <a href="https://publications.waset.org/abstracts/search?q=Nadia%20Smairi"> Nadia Smairi</a>, <a href="https://publications.waset.org/abstracts/search?q=Khaled%20Ghedira"> Khaled Ghedira</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Multiobjective Particle Swarm Optimization (MOPSO) has shown an effective performance for solving test functions and real-world optimization problems. However, this method has a premature convergence problem, which may lead to lack of diversity. In order to improve its performance, this paper presents a hybrid approach which embedded the MOPSO into the island model and integrated a local search technique, Variable Neighborhood Search, to enhance the diversity into the swarm. Experiments on two series of test functions have shown the effectiveness of the proposed approach. A comparison with other evolutionary algorithms shows that the proposed approach presented a good performance in solving multiobjective optimization problems. <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=migration" title=" migration"> migration</a>, <a href="https://publications.waset.org/abstracts/search?q=variable%20neighborhood%20search" title=" variable neighborhood search"> variable neighborhood search</a>, <a href="https://publications.waset.org/abstracts/search?q=multiobjective%20optimization" title=" multiobjective optimization"> multiobjective optimization</a> </p> <a href="https://publications.waset.org/abstracts/99544/an-enhanced-particle-swarm-optimization-algorithm-for-multiobjective-problems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/99544.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">1891</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">1890</span> Parallel Particle Swarm Optimization Optimized LDI Controller with Lyapunov Stability Criterion for Nonlinear Structural Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=P.%20W.%20Tsai">P. W. Tsai</a>, <a href="https://publications.waset.org/abstracts/search?q=W.%20L.%20Hong"> W. L. Hong</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20W.%20Chen"> C. W. Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20Y.%20Chen"> C. Y. Chen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we present a neural network (NN) based approach represent a nonlinear Tagagi-Sugeno (T-S) system. A linear differential inclusion (LDI) state-space representation is utilized to deal with the NN models. Taking advantage of the LDI representation, the stability conditions and controller design are derived for a class of nonlinear structural systems. Moreover, the concept of utilizing the Parallel Particle Swarm Optimization (PPSO) algorithm to solve the common P matrix under the stability criteria is given in this paper. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lyapunov%20stability" title="Lyapunov stability">Lyapunov stability</a>, <a href="https://publications.waset.org/abstracts/search?q=parallel%20particle%20swarm%20optimization" title=" parallel particle swarm optimization"> parallel particle swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20differential%20inclusion" title=" linear differential inclusion"> linear differential inclusion</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title=" artificial intelligence"> artificial intelligence</a> </p> <a href="https://publications.waset.org/abstracts/6974/parallel-particle-swarm-optimization-optimized-ldi-controller-with-lyapunov-stability-criterion-for-nonlinear-structural-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/6974.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">656</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">1889</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">1888</span> An Integration of Genetic Algorithm and Particle Swarm Optimization to Forecast Transport Energy Demand</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=N.%20R.%20Badurally%20Adam">N. R. Badurally Adam</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20R.%20Monebhurrun"> S. R. Monebhurrun</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Z.%20Dauhoo"> M. Z. Dauhoo</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Khoodaruth"> A. Khoodaruth</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Transport energy demand is vital for the economic growth of any country. Globalisation and better standard of living plays an important role in transport energy demand. Recently, transport energy demand in Mauritius has increased significantly, thus leading to an abuse of natural resources and thereby contributing to global warming. Forecasting the transport energy demand is therefore important for controlling and managing the demand. In this paper, we develop a model to predict the transport energy demand. The model developed is based on a system of five stochastic differential equations (SDEs) consisting of five endogenous variables: fuel price, population, gross domestic product (GDP), number of vehicles and transport energy demand and three exogenous parameters: crude birth rate, crude death rate and labour force. An interval of seven years is used to avoid any falsification of result since Mauritius is a developing country. Data available for Mauritius from year 2003 up to 2009 are used to obtain the values of design variables by applying genetic algorithm. The model is verified and validated for 2010 to 2012 by substituting the values of coefficients obtained by GA in the model and using particle swarm optimisation (PSO) to predict the values of the exogenous parameters. This model will help to control the transport energy demand in Mauritius which will in turn foster Mauritius towards a pollution-free country and decrease our dependence on fossil fuels. <p class="card-text"><strong>Keywords:</strong> <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=modeling" title=" modeling"> modeling</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=stochastic%20differential%20equations" title=" stochastic differential equations"> stochastic differential equations</a>, <a href="https://publications.waset.org/abstracts/search?q=transport%20energy%20demand" title="transport energy demand">transport energy demand</a> </p> <a href="https://publications.waset.org/abstracts/41078/an-integration-of-genetic-algorithm-and-particle-swarm-optimization-to-forecast-transport-energy-demand" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/41078.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">369</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">1887</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">1886</span> Optimal Allocation of Distributed Generation Sources for Loss Reduction and Voltage Profile Improvement by 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=Muhammad%20Zaheer%20Babar">Muhammad Zaheer Babar</a>, <a href="https://publications.waset.org/abstracts/search?q=Amer%20Kashif"> Amer Kashif</a>, <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Rizwan%20Javed"> Muhammad Rizwan Javed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Nowadays distributed generation integration is best way to overcome the increasing load demand. Optimal allocation of distributed generation plays a vital role in reducing system losses and improves voltage profile. In this paper, a Meta heuristic technique is proposed for allocation of DG in order to reduce power losses and improve voltage profile. The proposed technique is based on Multi Objective Particle Swarm optimization. Fewer control parameters are needed in this algorithm. Modification is made in search space of PSO. The effectiveness of proposed technique is tested on IEEE 33 bus test system. Single DG as well as multiple DG scenario is adopted for proposed method. Proposed method is more effective as compared to other Meta heuristic techniques and gives better results regarding system losses and voltage profile. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Distributed%20generation%20%28DG%29" title="Distributed generation (DG)">Distributed generation (DG)</a>, <a href="https://publications.waset.org/abstracts/search?q=Multi%20Objective%20Particle%20Swarm%20Optimization%20%28MOPSO%29" title=" Multi Objective Particle Swarm Optimization (MOPSO)"> Multi Objective Particle Swarm Optimization (MOPSO)</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=IEEE%20standard%20Test%20System" title=" IEEE standard Test System"> IEEE standard Test System</a> </p> <a href="https://publications.waset.org/abstracts/42467/optimal-allocation-of-distributed-generation-sources-for-loss-reduction-and-voltage-profile-improvement-by-using-particle-swarm-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/42467.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">454</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">1885</span> Thinned Elliptical Cylindrical Antenna Array Synthesis 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=Rajesh%20Bera">Rajesh Bera</a>, <a href="https://publications.waset.org/abstracts/search?q=Durbadal%20Mandal"> Durbadal Mandal</a>, <a href="https://publications.waset.org/abstracts/search?q=Rajib%20Kar"> Rajib Kar</a>, <a href="https://publications.waset.org/abstracts/search?q=Sakti%20P.%20Ghoshal"> Sakti P. Ghoshal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper describes optimal thinning of an Elliptical Cylindrical Array (ECA) of uniformly excited isotropic antennas which can generate directive beam with minimum relative Side Lobe Level (SLL). The Particle Swarm Optimization (PSO) method, which represents a new approach for optimization problems in electromagnetic, is used in the optimization process. The PSO is used to determine the optimal set of ‘ON-OFF’ elements that provides a radiation pattern with maximum SLL reduction. Optimization is done without prefixing the value of First Null Beam Width (FNBW). The variation of SLL with element spacing of thinned array is also reported. Simulation results show that the number of array elements can be reduced by more than 50% of the total number of elements in the array with a simultaneous reduction in SLL to less than -27dB. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=thinned%20array" title="thinned array">thinned array</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=Elliptical%20Cylindrical%20Array" title=" Elliptical Cylindrical Array"> Elliptical Cylindrical Array</a>, <a href="https://publications.waset.org/abstracts/search?q=Side%20Lobe%20Label." title=" Side Lobe Label."> Side Lobe Label.</a> </p> <a href="https://publications.waset.org/abstracts/4068/thinned-elliptical-cylindrical-antenna-array-synthesis-using-particle-swarm-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/4068.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">443</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">1884</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">1883</span> Drone Swarm Routing and Scheduling for Off-shore Wind Turbine Blades Inspection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohanad%20Al-Behadili">Mohanad Al-Behadili</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiang%20Song"> Xiang Song</a>, <a href="https://publications.waset.org/abstracts/search?q=Djamila%20Ouelhadj"> Djamila Ouelhadj</a>, <a href="https://publications.waset.org/abstracts/search?q=Alex%20Fraess-Ehrfeld"> Alex Fraess-Ehrfeld</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In off-shore wind farms, turbine blade inspection accessibility under various sea states is very challenging and greatly affects the downtime of wind turbines. Maintenance of any offshore system is not an easy task due to the restricted logistics and accessibility. The multirotor unmanned helicopter is of increasing interest in inspection applications due to its manoeuvrability and payload capacity. These advantages increase when many of them are deployed simultaneously in a swarm. Hence this paper proposes a drone swarm framework for inspecting offshore wind turbine blades and nacelles so as to reduce downtime. One of the big challenges of this task is that when operating a drone swarm, an individual drone may not have enough power to fly and communicate during missions and it has no capability of refueling due to its small size. Once the drone power is drained, there are no signals transmitted and the links become intermittent. Vessels equipped with 5G masts and small power units are utilised as platforms for drones to recharge/swap batteries. The research work aims at designing a smart energy management system, which provides automated vessel and drone routing and recharging plans. To achieve this goal, a novel mathematical optimisation model is developed with the main objective of minimising the number of drones and vessels, which carry the charging stations, and the downtime of the wind turbines. There are a number of constraints to be considered, such as each wind turbine must be inspected once and only once by one drone; each drone can inspect at most one wind turbine after recharging, then fly back to the charging station; collision should be avoided during the drone flying; all wind turbines in the wind farm should be inspected within the given time window. We have developed a real-time Ant Colony Optimisation (ACO) algorithm to generate real-time and near-optimal solutions to the drone swarm routing problem. The schedule will generate efficient and real-time solutions to indicate the inspection tasks, time windows, and the optimal routes of the drones to access the turbines. Experiments are conducted to evaluate the quality of the solutions generated by ACO. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=drone%20swarm" title="drone swarm">drone swarm</a>, <a href="https://publications.waset.org/abstracts/search?q=routing" title=" routing"> routing</a>, <a href="https://publications.waset.org/abstracts/search?q=scheduling" title=" scheduling"> scheduling</a>, <a href="https://publications.waset.org/abstracts/search?q=optimisation%20model" title=" optimisation model"> optimisation model</a>, <a href="https://publications.waset.org/abstracts/search?q=ant%20colony%20optimisation" title=" ant colony optimisation"> ant colony optimisation</a> </p> <a href="https://publications.waset.org/abstracts/141935/drone-swarm-routing-and-scheduling-for-off-shore-wind-turbine-blades-inspection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/141935.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">264</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">1882</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">1881</span> Design of Optimal Proportional Integral Derivative Attitude Controller for an Uncoupled Flexible Satellite 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=Martha%20C.%20Orazulume">Martha C. Orazulume</a>, <a href="https://publications.waset.org/abstracts/search?q=Jibril%20D.%20Jiya"> Jibril D. Jiya</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Flexible satellites are equipped with various appendages which vibrate under the influence of any excitation and make the attitude of the satellite to be unstable. Therefore, the system must be able to adjust to balance the effect of these appendages in order to point accurately and satisfactorily which is one of the most important problems in satellite design. Proportional Integral Derivative (PID) Controller is simple to design and computationally efficient to implement which is used to stabilize the effect of these flexible appendages. However, manual turning of the PID is time consuming, waste energy and money. Particle Swarm Optimization (PSO) is used to tune the parameters of PID Controller. Simulation results obtained show that PSO tuned PID Controller is able to re-orient the spacecraft attitude as well as dampen the effect of mechanical resonance and yields better performance when compared with manually tuned PID Controller. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Attitude%20Control" title="Attitude Control">Attitude Control</a>, <a href="https://publications.waset.org/abstracts/search?q=Flexible%20Satellite" title=" Flexible Satellite"> Flexible Satellite</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=PID%20Controller%20and%20Optimization" title=" PID Controller and Optimization"> PID Controller and Optimization</a> </p> <a href="https://publications.waset.org/abstracts/37412/design-of-optimal-proportional-integral-derivative-attitude-controller-for-an-uncoupled-flexible-satellite-using-particle-swarm-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/37412.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">401</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">1880</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">1879</span> Particle Swarm Optimization Based Method for Minimum Initial Marking in Labeled Petri Nets</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hichem%20Kmimech">Hichem Kmimech</a>, <a href="https://publications.waset.org/abstracts/search?q=Achref%20Jabeur%20Telmoudi"> Achref Jabeur Telmoudi</a>, <a href="https://publications.waset.org/abstracts/search?q=Lotfi%20Nabli"> Lotfi Nabli</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The estimation of the initial marking minimum (MIM) is a crucial problem in labeled Petri nets. In the case of multiple choices, the search for the initial marking leads to a problem of optimization of the minimum allocation of resources with two constraints. The first concerns the firing sequence that could be legal on the initial marking with respect to the firing vector. The second deals with the total number of tokens that can be minimal. In this article, the MIM problem is solved by the meta-heuristic particle swarm optimization (PSO). The proposed approach presents the advantages of PSO to satisfy the two previous constraints and find all possible combinations of minimum initial marking with the best computing time. This method, more efficient than conventional ones, has an excellent impact on the resolution of the MIM problem. We prove through a set of definitions, lemmas, and examples, the effectiveness of our approach. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=marking" title="marking">marking</a>, <a href="https://publications.waset.org/abstracts/search?q=production%20system" title=" production system"> production system</a>, <a href="https://publications.waset.org/abstracts/search?q=labeled%20Petri%20nets" title=" labeled Petri nets"> labeled Petri nets</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/98499/particle-swarm-optimization-based-method-for-minimum-initial-marking-in-labeled-petri-nets" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/98499.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">178</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">1878</span> Towards a Robust Patch Based Multi-View Stereo Technique for Textureless and Occluded 3D Reconstruction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ben%20Haines">Ben Haines</a>, <a href="https://publications.waset.org/abstracts/search?q=Li%20Bai"> Li Bai</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Patch based reconstruction methods have been and still are one of the top performing approaches to 3D reconstruction to date. Their local approach to refining the position and orientation of a patch, free of global minimisation and independent of surface smoothness, make patch based methods extremely powerful in recovering fine grained detail of an objects surface. However, patch based approaches still fail to faithfully reconstruct textureless or highly occluded surface regions thus though performing well under lab conditions, deteriorate in industrial or real world situations. They are also computationally expensive. Current patch based methods generate point clouds with holes in texturesless or occluded regions that require expensive energy minimisation techniques to fill and interpolate a high fidelity reconstruction. Such shortcomings hinder the adaptation of the methods for industrial applications where object surfaces are often highly textureless and the speed of reconstruction is an important factor. This paper presents on-going work towards a multi-resolution approach to address the problems, utilizing particle swarm optimisation to reconstruct high fidelity geometry, and increasing robustness to textureless features through an adapted approach to the normalised cross correlation. The work also aims to speed up the reconstruction using advances in GPU technologies and remove the need for costly initialization and expansion. Through the combination of these enhancements, it is the intention of this work to create denser patch clouds even in textureless regions within a reasonable time. Initial results show the potential of such an approach to construct denser point clouds with a comparable accuracy to that of the current top-performing algorithms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=3D%20reconstruction" title="3D reconstruction">3D reconstruction</a>, <a href="https://publications.waset.org/abstracts/search?q=multiview%20stereo" title=" multiview stereo"> multiview stereo</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=photo%20consistency" title=" photo consistency"> photo consistency</a> </p> <a href="https://publications.waset.org/abstracts/59669/towards-a-robust-patch-based-multi-view-stereo-technique-for-textureless-and-occluded-3d-reconstruction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/59669.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">1877</span> A Novel Approach of NPSO on Flexible Logistic (S-Shaped) Model for Software Reliability Prediction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pooja%20Rani">Pooja Rani</a>, <a href="https://publications.waset.org/abstracts/search?q=G.%20S.%20Mahapatra"> G. S. Mahapatra</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20K.%20Pandey"> S. K. Pandey</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we propose a novel approach of Neural Network and Particle Swarm Optimization methods for software reliability prediction. We first explain how to apply compound function in neural network so that we can derive a Flexible Logistic (S-shaped) Growth Curve (FLGC) model. This model mathematically represents software failure as a random process and can be used to evaluate software development status during testing. To avoid trapping in local minima, we have applied Particle Swarm Optimization method to train proposed model using failure test data sets. We drive our proposed model using computational based intelligence modeling. Thus, proposed model becomes Neuro-Particle Swarm Optimization (NPSO) model. We do test result with different inertia weight to update particle and update velocity. We obtain result based on best inertia weight compare along with Personal based oriented PSO (pPSO) help to choose local best in network neighborhood. The applicability of proposed model is demonstrated through real time test data failure set. The results obtained from experiments show that the proposed model has a fairly accurate prediction capability in software reliability. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=software%20reliability" title="software reliability">software reliability</a>, <a href="https://publications.waset.org/abstracts/search?q=flexible%20logistic%20growth%20curve%20model" title=" flexible logistic growth curve model"> flexible logistic growth curve model</a>, <a href="https://publications.waset.org/abstracts/search?q=software%20cumulative%20failure%20prediction" title=" software cumulative failure prediction"> software cumulative failure prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</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/36601/a-novel-approach-of-npso-on-flexible-logistic-s-shaped-model-for-software-reliability-prediction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/36601.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">344</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">1876</span> Model-Based Control for Piezoelectric-Actuated Systems Using Inverse Prandtl-Ishlinskii Model and Particle Swarm Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jin-Wei%20Liang">Jin-Wei Liang</a>, <a href="https://publications.waset.org/abstracts/search?q=Hung-Yi%20Chen"> Hung-Yi Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Lung%20Lin"> Lung Lin </a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper feedforward controller is designed to eliminate nonlinear hysteresis behaviors of a piezoelectric stack actuator (PSA) driven system. The control design is based on inverse Prandtl-Ishlinskii (P-I) hysteresis model identified using particle swarm optimization (PSO) technique. Based on the identified P-I model, both the inverse P-I hysteresis model and feedforward controller can be determined. Experimental results obtained using the inverse P-I feedforward control are compared with their counterparts using hysteresis estimates obtained from the identified Bouc-Wen model. Effectiveness of the proposed feedforward control scheme is demonstrated. To improve control performance feedback compensation using traditional PID scheme is adopted to integrate with the feedforward controller. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=the%20Bouc-Wen%20hysteresis%20model" title="the Bouc-Wen hysteresis model">the Bouc-Wen hysteresis model</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=Prandtl-Ishlinskii%20model" title=" Prandtl-Ishlinskii model"> Prandtl-Ishlinskii model</a>, <a href="https://publications.waset.org/abstracts/search?q=automation%20engineering" title=" automation engineering"> automation engineering</a> </p> <a href="https://publications.waset.org/abstracts/4325/model-based-control-for-piezoelectric-actuated-systems-using-inverse-prandtl-ishlinskii-model-and-particle-swarm-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/4325.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">514</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">1875</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> <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=particle%20swarm%20optimisation&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimisation&page=3">3</a></li> <li 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