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2015 International Power,Electronics and Materials Engineering Conference(IPEMEC 2015)

作者： Zhucai QIN Department of Automation Wuhan University of Technology

ISBN: (纸本)9781510806474

A cloud adaptive chaos particle swarm optimization algorithm is proposed for economic load dispatch problems of power system,which has the characteristics of nonlinear,non-convex and *** cloud generator was used to adaptively adjust the inertia weight of each particles,so as to optimize their optimization direction and improve the convergence speed of the algorithm;and the chaotic variation operation was introduced to adjust the particle's positions,so as to improve the diversity of the solution and avoid falling into local *** of 6 unit system demonstrates that the proposed algorithm has high accuracy and quick speed used in economic load dispatch of power system.

关键词： Economic Load Dispatch Cloud Adaptive Chaotic Variation particle swarm optimization algorithm

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Determine the optimal switching angles symmetrical cascaded multilevel inverter using Improved particle swarm optimization algorithm

Determine the optimal switching angles symmetrical cascaded ...

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International Symposium on Applied Machine Intelligence and Informatics (SAMI)

作者： Sajjad Ahmadi Mostafa Andalib Sahnesaraie Saeid Hojjati Talami Fakhteh Dini Mahnaz Mohebbi Zanganeh Yazdan Ashgevari Department of electrical engineering Islamic Azad University Ardabil Iran Alborz Electric Power Distribution Company Alborz Iran Department of electrical engineering Islamic Azad University Mehriz Iran Faculty of electrical & Computer engineering Shahid Beheshti University Tehran Iran

ISBN: (数字)9781728131498

ISBN: (纸本)9781728131504

With the increasing use of inverters in the industry, the need to reduce the output harmonics is felt more than ever. Interstellar multi-level structures have grown exponentially due to benefits such as harmonic reduction and loss reduction. In multilevel inverters, nonlinear and complex equations become more complex when the number of surfaces increases. One of the ways to find optimal keying angles is to reduce the harmonics of the whole Newton-Raphson method. One of the main disadvantages of the Newton-Raphson method is the strong dependence of the answers to the initial guesses. By choosing different initial guesses, different answers may be obtained or results will not converge. And their solution is not possible by conventional methods of numerical solution of nonlinear equations such as the Newton-Ruffson method. In this thesis, one of a variety of Intelligent algorithms is used to solve the Newton-Raphson method problem. The particle swarm algorithm has been selected based on the proper background in solving problems of high complexity to solve the problem of optimal keying angles in this dissertation. In this thesis, the particle swarm algorithm has been improved. Simulation results for various multilevel converters have been presented and represent the high efficiency of the improved type of particle swarm algorithm in determining the appropriate angle of fire for reducing the high harmonics and generating a waveform with very low harmonic distortion and near sinusoidal.

关键词： Multilevel Inverters Selective Harmonic Elimination particle swarm optimization algorithm Improved particle swarm optimization algorithm particle swarm algorithm Inventors Newton-Raphson method Theses multilayers harmonics Angle Optimal keying loss reduction

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Research on speed optimization model of pickling and tandem cold rolling mill based on particle swarm optimization algorithm

Research on speed optimization model of pickling and tandem ...

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第33届中国控制与决策会议

作者： ZHU Xiao-yan LIU Yong ZHANG Shi-bang LI Song-hua CAO Jiao-zhao HU Yun-jian Engineering Training Center Shenyang Jianzhu University School of Mechanical Engineering Shenyang Jianzhu University Information & Control Engineering Faculty Shenyang Jianzhu University

In the combined continuous pickling and tandem cold rolling line,the unstable speeds of four sections will not only decrease production efficiency,but also cause equipment *** order to solve the problem of great speed fluctuations in production line,the pickling section and rolling section are studied as a whole in this *** specific works include the analysis of the speed characteristics of each section and design of the objective ***,the optimized speed of each section is calculated by a particle swarm optimization *** to the speed comparison before and after optimization,it is found that the optimized speed does not fluctuate sharply,the acceleration and deceleration of the production line are stable,and the abundance values of loopers are controlled within a reasonable *** application shows that the optimized speeds can fluctuate well within a certain range,which reduces equipment wear and improves production *** proposed speed optimization model is suitable for industrial promotion.

关键词： Speed optimization particle swarm optimization algorithm Pickling and tandem cold rolling

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A Novel Quantum-behaved particle swarm optimization algorithm

A Novel Quantum-behaved Particle Swarm Optimization Algorith...

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The 14th International Symposium on Distributed Computing and Applications to Business,Engineering and Science(DCABES 2015)(第十四届分布式计算及其应用国际学术研讨会)

作者： Jing Zhao Hong Liu School of Information Qilu University of Technology Shandong Provincial Key Laboratory for Distribut Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology College of Inf

ISBN: (纸本)9781467365949

A novel Quantum-behaved particle swarm optimization algorithm with probability(P-QPSO)is introduced to improve the global convergence property of *** the proposed algorithm,all the particles keep the original evolution with large probability,and do not update the position of particles with small probability,and re-initialize the position of particles with small *** benchmark functions are used to test the performance of *** results of experiment show that the proposed technique can increase diversity of population and converge more rapidly than other evolutionary computation methods.

关键词： particle swarm optimization algorithm quantum-behaved probability benchmark function

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Hybrid particle swarm optimization with Bat algorithm 8

Hybrid Particle Swarm Optimization with Bat Algorithm

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8th International Conference on Genetic and Evolutionary Computing (ICGEC)

作者： Pan, Tien-Szu Dao, Thi-Kien Trong-The Nguyen Chu, Shu-Chuan Natl Kaohsiung Univ Appl Sci Dept Elect Engn Kaohsiung 807 Taiwan Haiphong Private Univ Fac Informat Technol Le Chan Hai Phong Vietnam Flinders Univ S Australia Sch Comp Sci Engn & Math Adelaide SA Australia

ISBN: (纸本)9783319122854

In this paper, a communication strategy for hybrid particle swarm optimization (PSO) with Bat algorithm (BA) is proposed for solving numerical optimization problems. In this work, several worst individuals of particles in PSO will be replaced with the best individuals in BA after running some fixed iterations, and on the contrary, the poorer individuals of BA will be replaced with the finest particles of PSO. The communicating strategy provides the information flow for the particles in PSO to communicate with the bats in BA. Six benchmark functions are used to test the behavior of the convergence, the accuracy, and the speed of the approached method. The results show that the proposed scheme increases the convergence and accuracy more than BA and PSO up to 3% and 47% respectively.

关键词： Hybrid particle swarm optimization with Bat algorithm particle swarm optimization algorithm Bat algorithm optimizations swarm Intelligence

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Test Case Generation for Modified Code using a Variant of particle swarm optimization (PSO) algorithm

Test Case Generation for Modified Code using a Variant of Pa...

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10th International Conference on Information Technology - New Generations (ITNG)

作者： Tiwari, Shailesh Mishra, K. K. Misra, A. K. MNNIT Allahabad CSED Allahabad Uttar Pradesh India

ISBN: (纸本)9780769549675

In this paper, a variant of particle swarm optimization (PSO) algorithm using modified time varying acceleration coefficients (PSO-TVAC) has been proposed and applied in creation of new test cases for modified code in regression testing. The performance of the proposed algorithm is compared with other existing PSO algorithms on five well known benchmark test functions. The experiments prove that the proposed algorithm has better performance. The test cases generated by the proposed PSO-TVAC algorithm have greater code coverage capability over the initial test cases.

关键词： regression testing particle swarm optimization algorithm test case generation

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Multi-Constrained Routing Based on particle swarm optimization and Fireworks algorithm 44

Multi-Constrained Routing Based on Particle Swarm Optimizati...

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44th Annual Conference of the IEEE Industrial-Electronics-Society (IECON)

作者： Hu, Youbing Wang, Kun Wan, Jinjiang Wang, Kaidong Hu, Xia Xidian Univ Sci & Technol Commun Networks Lab Shijiazhuang Sch Comp Sci & Technol Xian Peoples R China Xidian Univ Sch Comp Sci & Technol Xian Peoples R China Elect Technol Grp Corp Res Inst China 54 Shijiazhuang Hebei Peoples R China

ISBN: (纸本)9781509066841

This paper sets up a mathematical model that satisfies the multiconstrained routing optimization problem. By adding a penalty, multiple constraints are mapped to a fitness that satisfies multiple constraints. Then, it uses a heuristic routing algorithm based on particle swarm optimization (PSO) to perform heuristic routing search. Introducing the fireworks algorithm (FWA) based on the PSO search algorithm, our algorithm searches the optimal solution more quickly. Besides, it reduces the defect of PSO falling into the local optimum. Simulation shows the algorithm can effectively solve the multiconstrained routing problem in large-scale networks. While searching for optimal solutions, the success rate of the algorithm is about 5.21% higher than that of the standard PSO algorithm. That is improved by using the ant colony algorithm. The PSO-ACO algorithm is about 2.57% higher than the problem. The average cost of the final search is about 4.36% higher than that of the standard PSO algorithm. It is about 1.34% higher than the PSO-ACO algorithm improved by the ant colony algorithm.

关键词： multiple constraints routing Quality of Service penalty function particle swarm optimization algorithm fireworks algorithm

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Research on diversity of particle swarm optimization algorithm based on dynamic weight

Shiyou Huagong Gaodeng Xuexiao Xuebao/Journal of Petrochemic...

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Shiyou Huagong Gaodeng Xuexiao Xuebao/Journal of Petrochemical Universities 2008年第4期21卷 91-94页

作者： Zhu, Pei-Yi Zhang, Yu-Lin Department of Automation Changshu Institute of Technology Changshu 215500 China Department of Electric Information Engineering Huaiyin Institute of Technology Huaian 223003 China

Stocks diversity is the precondition for ensuring the convergence of PSO algorithm. The definition of stocks diversity is clear and the operand is small, moreover which was analyzed by particle evolution degree and aggregation degree. A changed algorithm was proposed based on adjusting weight adaptively. The algorithm ensures population diversity and avoids premature convergence effectively. Simulation results indicate that this algorithm not only speeds up the population the evolution speed, but also strengthens the algorithm the overall situation astringency, and convergence of probability also increases from 15% to 100%.

关键词： Diversity Dynamic weight particle swarm optimization algorithm Premature

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Logistics Distribution Location Based on particle swarm optimization algorithm

Logistics Distribution Location Based on Particle Swarm Opti...

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World Automation Congress

作者： Xichun CHEN Junli WANG School of Traffic and Transportation Engineering Lanzhou Jiaotong University Lanzhou 730070 China Gansu Province Supervision and Inspection Center of Product Quality Lanzhou 730030 China

ISBN: (纸本)9781467344975

The aim of logistics distribution center location is to improve the logistics service quality and reduce the transportation cost of goods. The problems are related to both the classical location problem and the vehicle routing problem. This paper proposes logistics distribution center location based on particle swarm optimization algorithm. It models the logistics distribution center location problem considering the user demand and operating cost, then it uses modified particle swarm optimization algorithm to give solution. Experimental result shows that the proposed model and algorithm is effective.

关键词： distribution center location particle swarm optimization algorithm optional point

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A particle swarm optimization-based algorithm for finding gapped motifs

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BIODATA MINING 2010年第1期3卷 9-9页

作者： Lei, Chengwei Ruan, Jianhua Univ Texas San Antonio Dept Comp Sci San Antonio TX 78249 USA

Background: Identifying approximately repeated patterns, or motifs, in DNA sequences from a set of co-regulated genes is an important step towards deciphering the complex gene regulatory networks and understanding gene functions. Results: In this work, we develop a novel motif finding algorithm (PSO+) using a population-based stochastic optimization technique called particle swarm optimization (PSO), which has been shown to be effective in optimizing difficult multidimensional problems in continuous domains. We propose a modification of the standard PSO algorithm to handle discrete values, such as characters in DNA sequences. The algorithm provides several features. First, we use both consensus and position-specific weight matrix representations in our algorithm, taking advantage of the efficiency of the former and the accuracy of the latter. Furthermore, many real motifs contain gaps, but the existing methods usually ignore them or assume a user know their exact locations and lengths, which is usually impractical for real applications. In comparison, our method models gaps explicitly, and provides an easy solution to find gapped motifs without any detailed knowledge of gaps. Our method allows the presence of input sequences containing zero or multiple binding sites. Conclusion: Experimental results on synthetic challenge problems as well as real biological sequences show that our method is both more efficient and more accurate than several existing algorithms, especially when gaps are present in the motifs.

关键词： particle swarm optimization particle swarm optimization algorithm Motif Finding Motif Length Real Motif

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