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GECCO
2006
Springer

Particle swarm with speciation and adaptation in a dynamic environment

9 years 1 months ago
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...
Xiaodong Li, Jürgen Branke, Tim Blackwell
Added 23 Aug 2010
Updated 23 Aug 2010
Type Conference
Year 2006
Where GECCO
Authors Xiaodong Li, Jürgen Branke, Tim Blackwell
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