A PARTICLE SWARM OPTIMIZATION ALGORITHM WITH DIFFERENTIAL EVOLUTION
Differential evolution (DE) is a simple evolutionary algorithm that has shown superior performance in the global continuous optimization.It mainly utilizes the differential information to guide its further search.But the differential information also results in instability of performance.Particle swarm optimization (PSO) has been developing rapidly and has been applied widely since it is introduced, as it can converge quickly.But PSO easily got stuck in local optima because it easily loses the diversity of swarm.This paper proposes a combination of DE and PSO (termed DEPSO) that makes up their disadvantages.DEPSO combines the differential information obtained by DE with the memory information extracted by PSO to create the promising solutions.Finally, DEPSO is tested to solve several benchmark optimization problems.The experimental results show the effectiveness of DEPSO algorithm for the multimodal function, and also verify that DEPSO can perform better than other algorithms (DE, CPSO) in solving the benchmark problems.
Differential evolution Particle swarm optimization Global minimization problem
ZHI-FENG HAO GUANG-HAN GUO HAN HUANG
School of Computer Science and Engineering, South China University of Technology, Guangzhou 510640 School of Mathematical Sciences, South China University of Technology, Guangzhou 510640
国际会议
2007 International Conference on Machine Learning and Cybernetics(IEEE第六届机器学习与控制论国际会议)
香港
英文
1031-1035
2007-08-19（万方平台首次上网日期，不代表论文的发表时间）