Particle swarm CMA evolution strategy for the optimization of multi-funnel landscapes
Research output: Contribution to book/conference proceedings/anthology/report › Conference contribution › Contributed › peer-review
Contributors
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 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 byconsidering 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-convexunderlying topology.
Details
Original language | English |
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Title of host publication | 2009 IEEE Congress on Evolutionary Computation, CEC 2009 |
Publisher | IEEE Xplore |
Pages | 2685-2692 |
Number of pages | 8 |
ISBN (print) | 9781424429592 |
Publication status | Published - 2009 |
Peer-reviewed | Yes |
Externally published | Yes |
Publication series
Series | 2009 IEEE Congress on Evolutionary Computation, CEC 2009 |
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Conference
Title | 2009 IEEE Congress on Evolutionary Computation, CEC 2009 |
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Duration | 18 - 21 May 2009 |
City | Trondheim |
Country | Norway |
External IDs
ORCID | /0000-0003-4414-4340/work/159608331 |
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