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CEC
2009
IEEE

Particle Swarm CMA Evolution Strategy for the optimization of multi-funnel landscapes

8 years 11 months ago
Particle Swarm CMA Evolution Strategy for the optimization of multi-funnel landscapes
— We extend the Evolution Strategy with Covariance Matrix Adaptation (CMA-ES) by collaborative concepts from Particle Swarm Optimization (PSO). The proposed Particle Swarm CMA-ES (PS-CMA-ES) algorithm is a hybrid realparameter algorithm that combines the robust local search performance of CMA-ES with the global exploration power of PSO using multiple CMA-ES instances to explore different parts of the search space in parallel. Swarm intelligence is introduced by considering individual CMA-ES instances as lumped particles that communicate with each other. This includes non-local information in CMA-ES, which improves the search direction and the sampling distribution. We evaluate the performance of PS-CMA-ES on the IEEE CEC 2005 benchmark test suite. The new PS-CMA-ES algorithm shows superior performance on noisy problems and multi-funnel problems with non-convex underlying topology.
Christian L. Müller, Benedikt Baumgartner, Iv
Added 20 May 2010
Updated 20 May 2010
Type Conference
Year 2009
Where CEC
Authors Christian L. Müller, Benedikt Baumgartner, Ivo F. Sbalzarini
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