New hybrid decomposition many-objective evolutionary algorithm. (Chinese. English summary) Zbl 1374.90337

Guo, Xiaofang; Wang, Yuping; Dai, Cai

J. Zhejiang Univ., Eng. Sci. 50, No. 7, 1313-1321 (2016).

Summary: A hybrid decomposition many-objective evolutionary algorithm based on a new dominance relation was proposed inspired by many-objective evolutionary algorithms based on decomposition in order to improve the diversity and convergence of the non-dominated solution set in many-objective optimization problems. The subpopulation evolutionary pattern was adopted, and a new efficiency order based dominance relation was designed to compare and update individuals inside each subpopulation, which helps to increase selective pressure and improve diversity. Powell search was used as the local search operator in order to improve the performance of local search. A hybrid evolution strategy combining traditional optimization method with evolutionary algorithm was adopted. Six standard benchmark problems with 5 to 20 objectives were tested to demonstrate the effectiveness of the algorithm. Experimental results showed that the algorithm performed better than other available algorithms in convergence and diversity.

MSC:

90C29	Multi-objective and goal programming
90C59	Approximation methods and heuristics in mathematical programming

Keywords:

multi-objective optimization problem; evolutionary algorithm; subpopulation; dominance relation