文献类型：期刊文献

英文题名：A hybrid co-evolutionary cultural algorithm based on particle swarm optimization for solving global optimization problems

作者：Sun, Yang[1]; Zhang, Lingbo[1]; Gu, Xingsheng[1]

机构：[1] Key Laboratory of Advanced Control and Optimization for Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China

年份：2012

卷号：98

起止页码：76

外文期刊名：Neurocomputing

收录：EI(收录号：20124115555964)

语种：英文

外文关键词：Problem solving - Benchmarking - Particle swarm optimization (PSO)

摘要：Intelligent evolutionary algorithms have been widely used to solve large-scale, complex global optimization problems. Co-evolutionary algorithm (CEA), cultural algorithm (CA), and particle swarm optimization (PSO) are all promising methods in the field of intelligent computation. In this paper, a hybrid co-evolutionary cultural algorithm based on particle swarm optimization (CECBPSO) is proposed. In CECBPSO, a novel space called shared global belief space (SGBS) is introduced into the co-evolutionary mechanism, and a new co-evolutionary cultural framework is built. Through the synergistic mechanism, the algorithm has higher probability of avoiding local optima and the whole swarm can find global optima more quickly. Factorial Design (FD) approach is used in this paper in order to get a guideline on how to tune the designed parameters in CECBPSO. Extensive computational studies are also carried out to evaluate the performance of CECBPSO on thirteen benchmark functions and three real-life optimization problems. The results show that the proposed algorithm has superior performance to other compared algorithms in terms of accuracy and convergence speed, especially on high-dimensional problems. ? 2012 Elsevier B.V.