Multiobjective Optimization with Fuzzy Classification-Assisted Environmental Selection.

Most environmental selection strategies in multiobjective evolutionary algorithms (MOEAs) select solutions based on their objective function values. However, the objective evaluations of many real-world problems are very time-consuming. The use of a large number of objective evaluations will inevitably reduce the efficiency of MOEAs. This paper proposes a fuzzy classification-assisted environmental selection (FAES) scheme to reduce the number of objective evaluations of MOEAs. The proposed method uses a fuzzy classifier to choose promising solutions in environmental selection. In the proposed method, first, solutions in the previous generations are classified into two classes using the Pareto dominance relation. The non-dominated solutions are positive class, and the dominated solutions are negative class. Next, the classified solutions are used to build a fuzzy classifier. Then, the built classifier is used to predict the membership degree of each of the current and offspring solutions. Only the offspring solutions, whose membership degrees to the positive class are larger than their parents’, are evaluated. The offspring solutions with smaller membership degrees are discarded with no objective evaluations. Therefore, the number of objective evaluations can be reduced. Finally, the evaluated offspring solutions are used in the environmental selection together with the current solutions. The proposed FAES strategy is integrated into an MOEA in computational experiments. Experimental results show the efficiency of the proposed FAES on reducing the number of objective evaluations.