Multimodal Optimization using Crowding Differential Evolution with Spatially Neighbors Best Search

Many real practical applications are often needed to find more than one optimum solution. Existing Evolutionary Algorithm (EAs) are originally designed to search the unique global value of the objective function. The present work proposed an improved niching based scheme named spatially neighbors best search technique combine with crowding-based differential evolution (SnbDE) for multimodal optimization problems. Differential evolution (DE) is known for its simple implementation and efficient for global optimization. Numerous DE-variants have been exploited to resolve diverse optimization problems. The proposed method adopts DE with DE/best/1/bin scheme. The best individual in the adopted scheme is searched around the considered individual to control the balance of exploitation and exploration. The results of the empirical comparison provide distinct evidence that SnbDE outperform the canonical crowding-based differential evolution. SnbDE has been shown to be efficient and effective in locating and maintaining multiple optima of selected benchmark functions for multimodal optimization problems.