An adaptive hybrid optimizer based on particle swarm and differential evolution for global optimization

作者：Xin Bin^*; Chen Jie; Peng ZhiHong; Pan Feng

来源：Science China Information Sciences, 2010, 53(5): 980-989.

DOI：10.1007/s11432-010-0114-9

• global optimization
• statistical learning
• differential evolution
• particle swarm optimization
• hybridization
• adaptation
• rotated function

This paper presents extensive experiments on a hybrid optimization algorithm (DEPSO) we recently developed by combining the advantages of two powerful population-based metaheuristics-differential evolution (DE) and particle swarm optimization (PSO). The hybrid optimizer achieves on-the-fly adaptation of evolution methods for individuals in a statistical learning way. Two primary parameters for the novel algorithm including its learning period and population size are empirically analyzed. The dynamics of the hybrid optimizer is revealed by tracking and analyzing the relative success ratio of PSO versus DE in the optimization of several typical problems. The comparison between the proposed DEPSO and its competitors involved in our previous research is enriched by using multiple rotated functions. Benchmark tests involving scalability test validate that the DEPSO is competent for the global optimization of numerical functions due to its high optimization quality and wide applicability.

• 出版日期2010-5
• 单位北京理工大学