A Multi-Swarm Self-Adaptive and Cooperative Particle Swarm Optimization

• Published in: Engineering applications of artificial intelligence Vol. 24; no. 6; pp. 958 - 967
• Main Authors: Zhang, Jiuzhong, Ding, Xueming
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.09.2011
• Subjects: Adaptive control systems; Adaptive inertia weight; Artificial intelligence; Cooperative strategy; Diversity operation; Expert systems; Exploration; Falling; Inertia; Multi-swarm; Optimization; Particle swarm optimization; Strategy; Multi-swarm; Diversity operation; Adaptive inertia weight; Particle swarm optimization; Cooperative strategy
• ISSN: 0952-1976; 1873-6769
• DOI: 10.1016/j.engappai.2011.05.010

In this paper, a Multi-Swarm Self-Adaptive and Cooperative Particle Swarm Optimization (MSCPSO) based on four sub-swarms is presented. In the proposed algorithm, several strategies are employed to avoid falling into local optimum, improve the diversity and achieve better solution. Particles in each sub-swarms share the only global historical best optimum to enhance the cooperative capability. Besides, the inertia weight of a particle in each sub-swarms is modified, which is subject to the fitness information of all particles, and the adaptive strategy is employed to control the influence of the historical information to create a more potential search ability. To effectively keep the balance between the global exploration and the local exploitation, the particle in each takes advantage of the shared information to maintain cooperation with each other and guides its own evaluation. On the other hand, in order to increase the diversity of the particles and avoid falling into a local optimum, a various diversity operation is adopted to guide the particles to jump out of the local optimum and achieve the global best position smoothly. The proposed method was applied to some well-known benchmarks; the results demonstrated good performances of MSCPSO in solving the complex multimodal functions.