On Some Properties of the lbest Topology in Particle Swarm Optimization
Particle Swarm Optimization (PSO) is arguably one of the most popular nature- inspired algorithms for real parameter optimization at present. The existing theoretical research on PSO is mostly based on the gbest (global best) particle topology, which usually is susceptible to false or premature convergence over multi-modal fitness landscapes. The present standard PSO (SPSO 2007) uses an lbest (local best) topology where a particle is stochastically attracted not towards the best position found in the entire swarm, but towards the best position found by any particle in its topological neighborhood. This paper presents a first step towards a probabilistic analysis of the lbest PSO with variable random neighborhood topology by addressing issues like inter-particle interaction and probabilities of selection based on particle ranks.
Particle Swarm Optimization Neighborhood Topologies Swarm Intelligence Global Optimization
Sayan Ghosh Debarati Kundu Kaushik Suresh Swagatam Das Ajith Abraham Bijaya. K. Panigrahi Václav Sná(s)el
Department of Electronics and Telecommunication Engineering, Jadavpur University, Kolkata, India Machine Intelligence Research Labs (MIR Labs), USA Department of Electrical Engineering, Indian Institute of Technology, Delhi, India Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Czech R
国际会议
2009 Ninth International Conference on Hybrid Intelligent Systems(第九届混合智能系统国际会议 HIS 2009)
沈阳
英文
1-6
2009-08-12（万方平台首次上网日期，不代表论文的发表时间）