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.
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