A novel grid-based differential evolution (DE) algorithm for many-objective optimization

In this paper, we propose a novel grid-based differential evolution (DE) algorithm termed as GrDE to handle many-objective optimization problems. For this algorithm, a novel differential evolution variant is formulated by first synthesizing an opposition-based self-adaptive DE operator with a local mutation operator, and then incorporating it into a grid-based framework. The proposed algorithm is being investigated through a comparative study with five other state-of-the-art evolutionary multi-objective optimization (EMO) algorithms using a total of 20 test instances from the DTLZ test suite. Through the experimental results that are presented by employing the Inverted Generational Distance (IGD) performance indicator, it is seen that GrDE is able to achieve competitive, if not better, performance when compared to the other algorithms used in this study.

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