Abstract
Differential evolution (DE) is a powerful evolutionary algorithm for global optimization problems. Generally, appropriate mutation strategies and proper equilibrium between global exploration and local exploitation are significant to the performance of DE. From this consideration, in this paper, we present a novel DE variant, abbreviated to DMIE-DE, to further enhance the optimization capacity of DE by developing a dual mutations collaboration mechanism with elites guiding and inferiors eliminating techniques. More specifically, an explorative mutation strategy DE/current-to-embest with an elite individual serving as part of the difference vector and an exploitative mutation strategy DE/ebest-to-rand with selecting an elite individual as the base vector are employed simultaneously to achieve the balance between local and global performance of the whole population instead of only one mutation strategy in classical DE algorithm. The control parameters F and CR for above mutation strategies are updated adaptively to supplement the optimization ability of DMIE-DE based on a rational probability distribution model and the successful experience from the previous iterations. Moreover, an inferior solutions eliminating technique is embedded to enhance the convergence speed and compensate cost of the fitness evaluation times during the evaluation process. To evaluate the performance of DMIE-DE, experiments are conducted by comparing with five state-of-the-art DE variants on solving 29 test functions in CEC2017 benchmark set. The experimental results indicate that the performance of DMIE-DE is significantly better than, or at least comparable to the considered DE variants.
Highlights
Differential evolution (DE), first proposed by Storn andPrice (Storn and Price, 1997), is a simple yet powerful evolutionary algorithm
In order to achieve a proper balance of the global exploration and local exploitation, we propose dual mutation strategies called Differential Evolution (DE)/current-to-embest and DE/ebestto-rand
To evaluate the performance of the proposed DMIEDE algorithm, comparative experiments are carried out based on 29 benchmark functions provided by CEC2017 platform, and the ith function is denoted by fi in this paper
Summary
Price (Storn and Price, 1997), is a simple yet powerful evolutionary algorithm. DE has exhibited notable performance due to its simple structure, rapid convergence speed as well as strong robustness and has been applied successfully in many domains of science and engineering such as neural network (Su et al, 2019; Baioletti et al, 2020), power system (Sakr et al, 2017; Reddy and Bijwe, 2019), medical aspect (Nunes et al, 2017; Song et al, 2019; Hosny et al, 2020), image processing (Paul and Das, 2015; Tarkhaneh and Shen, 2019). No matter which improvement strategy mentioned above is utilized, an appropriate tradeoff between the local exploitation and global exploration ability is an important guideline for the algorithm design, especially for the mutation operator, and excessive emphasis on one of them will adversely influence another. Based on this consideration, we propose a novel DE variant with double mutation strategies (DE/current-to-embest and DE/ebest-to-rand) and an inferior solution eliminating technique for further enhancing DE’s optimization ability.
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