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.

• Type: Conference
• Year: 2009
• Where: CEC
• Authors: Christian L. Müller, Benedikt Baumgartner, Ivo F. Sbalzarini
• Keywords: Artificial Intelligence, CEC 2009, CMA-ES Instances, Particle Swarm, Particle Swarm CMA-ES