A novel group search optimizer for multi-objective optimization

摘要

In this paper, a novel multi-objective group search optimizer named NMGSO is proposed for solving the multi-objective optimization problems. To simplify the computation, the scanning strategy of the original GSO is replaced by the limited pattern search procedure. To enrich the search behavior of the rangers, a special mutation with a controlling probability is designed to balance the exploration and exploitation at different searching stages and randomness is introduced in determining the coefficients of members to enhance the diversity. To handle multiple objectives, the non-dominated sorting scheme and multiple producers are used in the algorithm. In addition, the kernel density estimator is used to keep diversity. Simulation results based on a set of benchmark functions and comparisons with some methods demonstrate the effectiveness and robustness of the proposed algorithm, especially for the high-dimensional problems.