Improvement and Application of Particle Swarm Optimization Algorithm based on the Parameters and the Strategy of Co-Evolution

Authors

Haigang Li, School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, Jiangsu, ChinaFollow
Qian Zhang, School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, Jiangsu, ChinaFollow
Yong Zhang, School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, Jiangsu, ChinaFollow

Author Country (or Countries)

China

Abstract

PSO algorithm is an intelligent optimization algorithm based on swarm intelligence. Particle swarm optimization algorithm is simple, easy to implement, and it has a wide application prospect in scientific research and engineering applications. In real life, most of the optimization problem is the optimization problem of some nonlinear discrete with the existence of local. PSO algorithm also has some defects in treating optimization problem. The optimal performance of the PSO algorithm is efficiency; the attribute weights are optimized, which is the same as to improve the accuracy of case retrieval. The application of case is based reasoning in the optimization of pressure vessel model design. Through the experiment results, the optimization of the performance of PSO algorithm is better; the result of prediction is more approximate to the actual value, which can meet the needs of practical applications in engineering. The evolution strategy algorithm and the control parameters of the algorithm on the algorithm performance are affected. The control parameter adaptive particle swarm optimizer algorithm and evolution strategy of adaptive scheduling particle swarm algorithm, particle swarm optimization algorithm form a parameter and the strategy of co evolution, the co-evolution PSO algorithm and DBPSO algorithm and ASPSO algorithm are compared. The results show that, co-evolution PSO algorithm in the optimization performance improved to a certain extent than DBPSO algorithm and ASPSO algorithm, which achieved good results.

Recommended Citation

Li, Haigang; Zhang, Qian; and Zhang, Yong (2015) "Improvement and Application of Particle Swarm Optimization Algorithm based on the Parameters and the Strategy of Co-Evolution," Applied Mathematics & Information Sciences: Vol. 09: Iss. 3, Article 30.
Available at: https://digitalcommons.aaru.edu.jo/amis/vol09/iss3/30