HS-ES-DE: HS-ES Followed by L-SHADE-EpSin for Real Parameter Single Objective Optimization

分类： 计算机科学 >> 计算机软件 提交时间： 2022-12-07

Jianjun Jiang Yong Wan Chengjun Li Chuchuan Cen Yikai Shao Qinxue Meng

摘要： For real parameter single objective optimization, Differential Evolution (DE) and Covariance Matrix Adaptation Evolution Strategy (CMA-ES) both perform powerfully. Nevertheless, in the field of real parameter single objective optimization, it is impossible for a given algorithm to perform well in all fitness landscapes. Practice has proved that ensemble of different algorithms may lead to improvement in solution. In this paper, based on two famous population-based metaheuristics - LSHADE-EpSin and HS-ES, we propose ensemble with successively executed constituent algorithms - HS-ES-DE. In our algorithm, HS-ES is replaced by L-SHADE-EpSin after stagnation is detected. Beside our HS-ES-DE, 12 population-based metaheuristics are involved in our experiments in which three benchmark test suites are employed. Experimental results show that our algorithm is very competitive.

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