A region search evolutionary algorithm for many-objective optimization

Abstract

Achieving a balance between convergence and diversity in many-objective optimization is a great challenge. This paper suggests an evolutionary algorithm based on a region search strategy to deal with different kinds of benchmark problems. In the proposed algorithm, each solution is associated with a region, and the region search strategy is applied to constrain the updating process; this strategy will enhance the diversity of population without losing convergence. A new normalization procedure is used for dealing with scaled problems. Moreover, the comparison of two solutions is based on both dominance relation and perpendicular distance; the result shows the algorithm's reliability for solving both convex and concave problems. The performance of the proposed algorithm is validated by several well-known benchmark problems with different properties. Seven state-of-the-art algorithms are compared and the experimental results demonstrate that the introduced algorithm performs the best on almost all benchmark problems. Furthermore, the proposed strategy depicts a high computational efficiency for solving the problems with a high dimension of objectives.