A novel algorithm for non-dominated hypervolume-based multiobjective optimization

Hypervolume indicator is a commonly accepted quality measure to assess the set of non-dominated solutions obtained by an evolutionary multiobjective optimization algorithm. Recently, an emerging trend in the design of evolutionary multiobjective optimization algorithms is to directly optimize a quality indicator. In this paper, we propose a hypervolume-based evolutionary algorithm for multiobjective optimization. There are two main contributions of our approach, on one hand, a unique fitness assignment strategy is proposed, on the other hand, we design a slicing based method to calculate the exclusive hypervolume of each individual for environmental selection. From an extensive comparative study with three other MOEAs on a number of two and three objective test problems, it is observed that the proposed algorithm has good performance in convergence and distribution.