Particle swarm with speciation and adaptation in a dynamic environment

This paper describes an extension to a speciation-based particle swarm optimizer (SPSO) to improve performance in dynamic environments. The improved SPSO has adopted several proven useful techniques. In particular, SPSO is shown to be able to adapt to a series of dynamic test cases with varying number of peaks (assuming maximization). Inspired by the concept of quantum swarms, this paper also proposes a particle diversification method that promotes particle diversity within each converged species. Our results over the moving peaks benchmark test functions suggest that SPSO incorporating this particle diversification method can greatly improve its adaptability hence optima tracking performance. Categories and Subject Descriptors G.1 [Numerical Analysis]: Optimization; F.2.1 [Analysis of Algorithms and Problem Complexity]: Numerical Algorithms and Problems General Terms Algorithms, Performance, Experimentation Keywords Evolutionary Computation, Swarm Intelligence, Optimization in Dynami...

• Type: Conference
• Year: 2006
• Where: GECCO
• Authors: Xiaodong Li, Jürgen Branke, Tim Blackwell
• Keywords: Dynamic Environments, GECCO 2006, Optimization, Particle Diversification Method, Particle Swarm Optimizer