Novel mutation strategy for enhancing SHADE and LSHADE algorithms for global numerical optimization

• Published in: Swarm and evolutionary computation Vol. 50; p. 100455
• Main Authors: Mohamed, Ali W., Hadi, Anas A., Jambi, Kamal M.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.11.2019
• Subjects: Differential evolution; Evolutionary computation; Global optimization; Novel ordered mutation; Differential evolution; Global optimization; Evolutionary computation; Novel ordered mutation
• ISSN: 2210-6502
• DOI: 10.1016/j.swevo.2018.10.006

Proposing new mutation strategies to improve the optimization performance of differential evolution (DE) is an important research study. Therefore, the main contribution of this paper goes in three directions: The first direction is introducing a less greedy mutation strategy with enhanced exploration capability, named DE/current-to-ord_best/1 (ord stands for ordered) or ord_best for short. In the second direction, we introduce a more greedy mutation strategy with enhanced exploitation capability, named DE/current-to-ord_pbest/1 (ord_pbest for short). Both of the proposed mutation strategies are based on ordering three selected vectors from the current generation to perturb the target vector, where the directed differences are used to mimic the gradient decent behavior to direct the search toward better solutions. In ord_best, the three vectors are selected randomly to enhance the exploration capability of the algorithm. On the other hand, ord_pbest is designed to enhance the exploitation capability where two vectors are selected randomly and the third is selected from the global p best vectors. Based on the proposed mutation strategies, ord_best and ord_pbest, two DE variants are introduced as EDE and EBDE, respectively. The third direction of our work is a hybridization framework. The proposed mutations can be combined with DE family algorithms to enhance their search capabilities on difficult and complicated optimization problems. Thus, the proposed mutations are incorporated into SHADE and LSHADE to enhance their performance. Finally, in order to verify and analyze the performance of the proposed mutation strategies, numerical experiments were conducted using CEC2013 and CEC2017 benchmarks. The performance was also evaluated using CEC2010 designed for Large-Scale Global Optimization. Experimental results indicate that in terms of robustness, stability, and quality of the solution obtained, both mutation strategies are highly competitive, especially as the dimension increases. •Two mutation operators are introduced, ord_best and ord_pbest, which are versions of the classical DE/current-to-best/1 scheme.•The proposed mutations were incorporated into SHADE and LSHADE algorithms in order to enhance their performances.•Numerical experiments were conducted using CEC2013 and CEC2017 benchmarks. The performance was also evaluated using CEC2010 designed for Large-Scale Global Optimization.•The experimental results indicate that the proposed mutations enhanced the global and local search capabilities and increase the convergence speed of the new algorithm compared with the classical mutation.