A Particle Swarm Optimization Algorithm with Differential Evolution

Abstract

Differential evolution (DE) is a simple evolutionaryalgorithm 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.

Hybridizing Differential Evolution and Particle Swarm Optimization to Design Powerful Optimizers: A Review and Taxonomy

“…Recently, Hao et al constructed a new DEPSO (namely DEPSO-HGH) [39]. In this hybrid optimizer, DE and PSO are regarded as two operators to generate candidate solutions, and they act on the level of dimensional components of individuals.…”

“…They would also like to thank the two authors of [39], Prof. Z. F. Hao and Mr. G. H. Guo, who provided very useful materials about their hybrid algorithm.…”

Hybrid Differential Evolution - Particle Swarm Optimization Algorithm for Solving Global Optimization Problems

“…Omran et al [6] developed a hybrid version consisting of Barebones PSO and DE. In Zhi Feng et al [7] hybrid version, the candidate solution is generated either by DE or by PSO according to some fixed probability distribution. In this paper we propose a simple hybrid version of DE and PSO, called DE-PSO.…”

Harmonic Potential Fields: an Effective Tool for Generating a Self-Organizing Behavior

“…Unfortunately formulating the problem in this manner for a large group of agents leads to an intractable situation. Self-organizing optimization methods (Wu & Chow, 2007;Kohonen, 1997;Lampinen & Storn, 2004;Hao et al, 2007) may be used for such a purpose. They are known for their ability to handle nonlinear functions having large degrees of freedom.…”