![]() MMRDE is tested with some improved DE variants based on 28 benchmark functions for real-parameter optimization that have been recommended by the Institute of Electrical and Electronics Engineers CEC2013 special session. We use roulette wheel selection to arrange mutation strategies based on their success rates for each individual. Therefore, we add two basic strategies to help maintain population diversity and increase the robustness. Although our reflection-based mutation strategy can balance exploration and exploitation of DE, it is still prone to premature convergence or evolutionary stagnation when solving complex multimodal optimization problems. We design an experiment to compare its performance with seven mutation strategies, and we prove its effectiveness at balancing exploration and exploitation of DE. We first propose a new, reflection-based mutation operation inspired by the reflection operations in the Nelder–Mead method. In this paper, we propose a differential evolution (DE) algorithm variant with a combination of multiple mutation strategies based on roulette wheel selection, which we call MMRDE.
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