Novel multimodal problems and differential evolution with ensemble of restricted tournament selection

Multi-modal optimization refers to locating not only one optimum but a set of locally optimal solutions. Niching is an important technique to solve multi-modal optimization problems. The ability of discover and maintain multiple niches is the key capability of these algorithms. In this paper, differential evolution with an ensemble of restricted tournament selection (ERTS-DE) algorithm is introduced to perform multimodal optimization. The algorithms is tested on 15 newly designed scalable benchmark multi-modal optimization problems and compared with the crowding differential evolution (Crowding-DE) in the literature. As shown by the experimental results, the proposed algorithm outperforms the Crowding-DE on the novel scalable benchmark problems.

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