Abstract
Differential evolution (DE) is one of the evolutionary algorithms. DE has excellent ability for searching global optimum over continuous spaces. This paper introduces a modified DE algorithm called the differential evolution with adaptive neighborhood for locating all the global optima of multimodal functions. The proposed method form a neighborhood using euclidean distance between individuals, re-form the neighborhood at prescribed generations. The proposed method can independently locate global optima in each neighborhood by dividing population into multiple neighborhood. In real world optimization problems, multiple global optima and local optima are required frequently, along with an unique global optimum. This proposed method is one of improvements for these real world optimization problems. The performance of the proposed method is evaluated with several multimodal function optimization problems. The experimental results show good performance better than original DE algorithm.
