Gang Yao
PetroChina Research Institute of Petroleum Exploration & Development-Northwest

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A Improved Particle Swarm Optimization Algorithm with Dynamic Acceleration Coefficients Gang Ma; Renbin Gong; Qun Li; Gang Yao
Bulletin of Electrical Engineering and Informatics Vol 5, No 4: December 2016
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (767.308 KB) | DOI: 10.11591/eei.v5i4.561

Abstract

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.
A Improved Particle Swarm Optimization Algorithm with Dynamic Acceleration Coefficients Gang Ma; Renbin Gong; Qun Li; Gang Yao
Bulletin of Electrical Engineering and Informatics Vol 5, No 4: December 2016
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (767.308 KB) | DOI: 10.11591/eei.v5i4.561

Abstract

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.
A Improved Particle Swarm Optimization Algorithm with Dynamic Acceleration Coefficients Gang Ma; Renbin Gong; Qun Li; Gang Yao
Bulletin of Electrical Engineering and Informatics Vol 5, No 4: December 2016
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (767.308 KB) | DOI: 10.11591/eei.v5i4.561

Abstract

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.