SaMDE: A Self Adaptive Choice of DNDE and SPIDE Algorithms with MRLDE

Abstract

Differential evolution (DE) is a proficient optimizer and has been broadly implemented in real life applications of various fields. Several mutation based adaptive approaches have been suggested to improve the algorithm efficiency in recent years. In this paper, a novel self-adaptive method called SaMDE has been designed and implemented on the mutation-based modified DE variants such as modified randomized localization-based DE (MRLDE), donor mutation based DE (DNDE), and sequential parabolic interpolation based DE (SPIDE), which were proposed by the authors in previous research. Using the proposed adaptive technique, an appropriate mutation strategy from DNDE and SPIDE can be selected automatically for the MRLDE algorithm. The experimental results on 50 benchmark problems taken of various test suits and a real-world application of minimization of the potential molecular energy problem validate the superiority of SaMDE over other DE variations.

Improved Differential Evolution Algorithm Guided by Best and Worst Positions Exploration Dynamics

“…Xiang et al [42] combined two mutation strategies, DE/current/1/bin and DE/p-best/bin/1, to enhance the performance of the DE algorithm. Some recent research on the development of mutation operations is included in [43][44][45][46][47][48][49].…”