China University of Geosciences, School of Computer Science, Wuhan, 430078, China.
Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan, 430078, China.
Sci Rep. 2022 Jan 19;12(1):986. doi: 10.1038/s41598-021-04549-1.
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.
在基于群体的元启发式算法中，差分进化（DE）和协方差矩阵自适应进化策略（CMA-ES）在实数单目标优化方面表现出色。与 DE 相比，CMA-ES 在许多情况下更早地陷入停滞。在本文中，我们提出了基于 DE 的个体再分配的 CMA-ES，即 IR-CMA-ES，以解决 CMA-ES 中的停滞问题。我们基于两个基准测试套件执行实验，将我们的算法与九个同行进行比较。实验结果表明，我们的 IR-CMA-ES 在实数单目标优化领域具有竞争力。
