AI Article Synopsis
- Both Differential Evolution (DE) and Covariance Matrix Adaptation Evolution Strategy (CMA-ES) are effective metaheuristics for optimizing real parameters in single objective problems, but CMA-ES often stagnates earlier.
- This paper introduces IR-CMA-ES, a modified version of CMA-ES that incorporates individual redistribution from DE to combat stagnation.
- Experiments conducted on benchmark test suites demonstrate that IR-CMA-ES performs competitively compared to nine other optimization algorithms.
Article Abstract
Among population-based metaheuristics, both Differential Evolution (DE) and Covariance Matrix Adaptation Evolution Strategy (CMA-ES) perform outstanding for real parameter single objective optimization. Compared with DE, CMA-ES stagnates much earlier in many occasions. In this paper, we propose CMA-ES with individuals redistribution based on DE, IR-CMA-ES, to address stagnation in CMA-ES. We execute experiments based on two benchmark test suites to compare our algorithm with nine peers. Experimental results show that our IR-CMA-ES is competitive in the field of real parameter single objective optimization.
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Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8770519 | PMC |
| http://dx.doi.org/10.1038/s41598-021-04549-1 | DOI Listing |
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