L-SHADE-E: Ensemble of two differential evolution algorithms originating from L-SHADE

• Published in: Information sciences Vol. 552; pp. 201 - 219
• Main Authors: Wang, Xinxin, Li, Chengjun, Zhu, Jiarui, Meng, Qinxue
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.04.2021
• Subjects: Differential evolution; Ensemble; Real parameter single objective optimization; Switchover; Differential evolution; Switchover; Real parameter single objective optimization; Ensemble
• ISSN: 0020-0255; 1872-6291
• DOI: 10.1016/j.ins.2020.11.055

For real parameter single objective optimization, Differential Evolution (DE) performs better than other types of population-based metaheuristic. Nevertheless, in the field of DE, it is impossible for a given combination of operators to perform well in all fitness landscapes. Practice has proved that assembling more than one combinations of operators may lead to improvement in solution. However, some DE algorithms with good performance, e.g., one of the best performers in competitions of real parameter single objective optimization among population-based metaheuristics held by the series of IEEE Congress on Evolutionary Computation - L-SHADE-EpSin, are still not involved in ensemble. Furthermore, ensemble based on similar DE algorithms is rarely reported. Based on such a background, we propose L-SHADE-E, ensemble of L-SHADE-EpSin and L-SHADE-RSP in this paper. The two constituent algorithms are both variants of L-SHADE and belong to similar DE algorithms. In our algorithm, L-SHADE-EpSin or L-SHADE-RSP is selected randomly to run at the beginning. If progress on fitness is low for generations, the other constituent algorithm takes over population immediately. Our experiments are based on the CEC 2014 and CEC 2017 benchmark test suites. Altogether, our algorithm is compared with nine DE algorithms and three population-based metaheuristics other than DE. Experimental results show that our algorithm is very competitive.