A Improved Particle Swarm Optimization Algorithm with Dynamic Acceleration Coefficients
| Autor: | Ma Gang, Yao Gang, Li Qun, Gong Renbin |
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| Rok vydání: | 2016 |
| Předmět: |
Mathematical optimization
Control and Optimization Computer Networks and Communications Heuristic (computer science) Computer Science::Neural and Evolutionary Computation MathematicsofComputing_NUMERICALANALYSIS 0507 social and economic geography ComputingMethodologies_ARTIFICIALINTELLIGENCE 01 natural sciences 010305 fluids & plasmas Robustness (computer science) 0103 physical sciences Convergence (routing) Computer Science (miscellaneous) Local search (optimization) Electrical and Electronic Engineering Multi-swarm optimization Instrumentation Mathematics business.industry 05 social sciences Particle swarm optimization Maxima and minima Hardware and Architecture Control and Systems Engineering business 050703 geography Algorithm Information Systems Premature convergence |
| Zdroj: | Bulletin of Electrical Engineering and Informatics. 5:474-480 |
| ISSN: | 2302-9285 2089-3191 |
| DOI: | 10.11591/eei.v5i4.561 |
| Popis: | Particle swarm optimization (PSO) is one of the famous heuristic methods. However, this method may suffer to trap at local minima especially for multimodal problem. This paper proposes a modified particle swarm optimization with dynamic acceleration coefficients (ACPSO). To efficiently control the local search and convergence to the global optimum solution, dynamic acceleration coefficients are introduced to PSO. To improve the solution quality and robustness of PSO algorithm, a new best mutation method is proposed to enhance the diversity of particle swarm and avoid premature convergence. The effectiveness of ACPSO algorithm is tested on different benchmarks. Simulation results found that the proposed ACPSO algorithm has good solution quality and more robust than other methods reported in previous work. |
| Databáze: | OpenAIRE |
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