Particle swarm CMA evolution strategy for the optimization of multi-funnel landscapes

Authors:
Christian L. Müller;Benedikt Baumgartner;Ivo F. Sbalzarini
Affiliations:
Institute of Theoretical Computer Science and Swiss Institute of Bioinformatics, ETH Zurich, Zürich, Switzerland;Robotics and Embedded Systems Group, Department of Informatics, Technische Universität München;Institute of Theoretical Computer Science and Swiss Institute of Bioinformatics, ETH Zurich, Zürich, Switzerland
Venue:
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Year:
2009

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Abstract

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 real parameter 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 nonlocal 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 nonconvex underlying topology.