A decomposition-rotation dominance based evolutionary algorithm with reference point adaption for many-objective optimization. (1st April 2023) - Zhang, Wei; Liu, Jianchang; Tan, Shubin; Wang, Honghai
Abstract: Evolutionary multi-objective optimization aims at obtaining a set of Pareto-optimal solutions among the multiple conflicting objectives. However, the ability of multi-objective evolutionary algorithm to converge towards the Pareto front and maintain population diversity often seriously decrease with the number of objectives increasing. To address this problem, we propose a decomposition-rotation dominance based evolutionary algorithm with reference point adaption for many-objective optimization, termed DREA. In the DREA, a dominance relation based on decomposition and rotation (DR-dominance) is proposed for increasing selection pressure and maintaining diversity simultaneously, which is achieved by decomposing the objective space and rotating the coordinate system. At the same time, a reference point adaption strategy is designed, which can adapt well to different types of Pareto fronts. In addition, an effective mating selection strategy is proposed for enhancing the probability of parents with good convergence and diversity combination. The experimental results on several commonly used benchmark test problems with objective numbers varying from 5 to 20 and two real-world engineering applications have demonstrated that the proposed DREA is highly competitive in solving many-objective optimization. Highlights: A decomposition and rotation-based dominance relation is proposed. An improved penalty boundary intersection (IPBI) is designed. A reference point adaption strategy is designed to adapt to various Pareto fronts. An effective mating selection strategy is proposed.