A Collaborative and Adaptive Approach to Particle Swarm Optimization

• Main Authors: Xiang-Han Chen, 陳詳翰
• Other Authors: Wei-Ping Lee
• Format: Others
• Language: en_US
• Published: 2007
• Online Access: http://ndltd.ncl.edu.tw/handle/20613399819386049472

碩士 === 中原大學 === 資訊管理研究所 === 95 === This paper presents a modified of particle swarm optimizations (PSOs), the collaborative and adaptive particle swarm optimization (CAPSO), which uses a novel communication and learning strategy whereby elitist particles’ positional dispersive information is used to influence all particles’ velocity. In order to improve the performance of PSO and maintain particle’s diversity based on randomization, adaptive constriction factors and the triad-attractive operation were brought forward. This strategy enables the diversity of the swarm to be preserved to faster convergence and accuracy. Experiments were conducted on multimodal test functions and traveling salesman problem (TSP). The results demonstrate good performance of the CAPSO in solving multimodal problems and combinatorial optimization problem when compared with other PSOs.