Hybridizing the Pareto Multi-Objective Optimization Evolutionary Algorithms by Means of Multi-Objective Local Search

Hybridizing of evolutionary algorithms (EA) by means of local search has shown considerable performance improvement in single-objective optimization (SOO) field. The fine search in the neighborhood of the EA individuals (solutions) allows a fine exploration of the solution space. This paper investigates the application and the evaluation of the hybridizing mechanism of the EAs in the multi-objective optimization (MOO) domain. For this hybridizing, two types of multi-objective local search (MOLS) are used; namely large- and narrow-MOLS. Among numerous possible multi-objective optimization EAs (MOEAs), the Pareto-based variants have been considered. The performance of hybrid MOO variants are evaluated by solving the multi-objective knapsack problem.