Hybrid Evolutionary Multi-Objective Optimization Algorithms

This paper examines how the search ability of evolutionary multi-objective optimization (EMO) algorithms can be improved by the hybridization with local search through computational experiments on multi-objective permutation flowshop scheduling problems. The task of EMO algorithms is to find a variety of nondominated solutions of multi-objective optimization problems. First we describe our multi-objective genetic local search (MOGLS) algorithm, which is the hybridization of a simple EMO algorithm with local search. Next we discuss some implementation issues of local search in our MOGLS algorithm such as the choice of initial (i.e., starting) solutions for local search and a termination condition of local search. Then we implement hybrid EMO algorithms using well-known EMO algorithms: SPEA and NSGA-II. Finally we compare those EMO algorithms with their hybrid versions through computational experiments. Experimental results show that the hybridization with local search can improve the search ability of the EMO algorithms when local search is appropriately implemented in their hybrid versions.