关键词: 约束优化, 佳点集, 粒子群算法, 协同进化

Abstract: To overcome the weakness of over-concentration when the population of Particle Swarm Optimization (PSO) is initialized and the search precision of basic PSO is not high, an Improved PSO (IPSO) for constrained optimization problems was proposed. A technique of Good Point Set (GPS) was introduced to distribute the initialized particles evenly and the population with diversity would not fall into the local extremum. Co-evolutionary method was utilized to maintain communication between the two populations; thereby the search accuracy of PSO was increased. The simulation results indicate that, the proposed algorithm obtains the theoretical optimal solutions on the test of five benchmark functions used in the paper and the statistical variances of four of them are 0. The proposed algorithm improves the calculation accuracy and robustness and it can be widely used in the constrained optimization problems.

Key words: constrained optimization, good point set, particle swarm optimization, co-evolution