A Weight Vector Bi-Objective Evolutionary Algorithm with Bi-criterion Evolution for Many-Objective Optimization - Wang, Jiangtao; Chen, Hanning (2022 IEEE 4th International Conference on Power, Intelligent Computing and Systems (ICPICS), 2022)
In the multi-objective optimization process, its main purpose is to obtain Pareto non-dominated solutions with well convergence and diversity, but in most cases the convergence and diversity of solutions are conflicting. In order to solve this problem, this paper proposes a new convergence and diversity evaluation method to convert the multi-objective optimization problem into a bi-goal, more effectively evaluate the dominant relationship between individuals through the weight vectors, and then increase the selection pressure. We introduce the bi-criterion evolution can better balance the convergence and diversity of Pareto optimal solutions. Based on the proposed method, a new multi-objective optimization algorithm BiGE-BEW is proposed. Experimental results show that the proposed algorithm shows strong competitiveness in solving multi-objective optimization problems and greatly improves the performance of algorithms for solving multi-objective optimization problems.