A hybrid multiobjective evolutionary algorithm: Striking a balance with local search

This paper presents a hybrid multiobjective evolutionary algorithm (HMEA) that efficiently deals with multiobjective optimization problems (MOPs). The aim is to discover new nondominated solutions in the neighborhood of the most promising individuals in order to effectively push individuals toward the global Pareto front. It can be achieved by bringing the strength of an adaptive local search (ALS) to bear upon the evolutionary multiobjective optimization. The ALS is devised by combining a weighted fitness strategy and a knowledge-based local search which does not incur any significant computational cost. To be more exact, the highly converged and less crowded solutions selected in accordance with the weighted fitness values are improved by the local search, thereby helping multiobjective evolutionary algorithms (MEAs) to economize on the search time and traverse the search space. Thus, the proposed HMEA that transplants the ALS to the framework of MEAs can achieve higher proximity and better diversity of nondominated solutions. To show the utility of HMEA, the ALS for multiobjective knapsack problems (MKPs) is developed by exploiting the problem's knowledge. Experimental results on the MKPs have provided evidence for its effectiveness as regards the proximity and the diversity performances.