Particle Swarm Optimization based on self-organizing topology driven by fitness
To explore the relation between topologic characteristics of dynamic network and performance of the particle swarm optimization (PSO) algorithm, the population of PSO is viewed as a network where each particle is represented as a node and the network structure changes dynamically as the fitness of particles varies. Moreover, in this paper, the structural changes involve adding and removing the links but the network size remains the same. Then, two kinds of simulations are conducted. The results from one kind focusing on PSO show that the dynamic network is capable of balancing exploration and exploitation so that the performance of PSO can be improved as long as the weight θis selected properly. In addition, the results from other kind concerning on topologic characteristics of dynamical network indicate the impact of network structure on algorithm behavior and the law of network evolution.
particle swarm optimization algorithm selforganizing topology driven by fitness topologic characteristic
Simin Mo Janchao Zeng Ying Tan
College of Electrical and Information Engineering Lanzhou University of Technology Lanzhou, Ganshu , Complex System and Computational Intelligence Laboratory Taiyuan University of Science and Technolog
国际会议
International Conference on Computational Aspects of Social Networks(国际社会网络计算会议 CASoN 2010)
太原
英文
23-26
2010-09-26（万方平台首次上网日期，不代表论文的发表时间）