Improved Memetic NSGA-II Using a Deep Neighborhood Search

Samir Mahdi and Brahim Nini
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Samir Mahdi: University Larbi Ben M'hidi Oum El Bouaghi, Algeria
Brahim Nini: University Larbi Ben M'hidi Oum El Bouaghi, Algeria

International Journal of Applied Metaheuristic Computing (IJAMC), 2021, vol. 12, issue 4, 138-154

Abstract: Elitist non-sorted genetic algorithms as part of Pareto-based multi-objective evolutionary algorithms seems to be one of the most efficient algorithms for multi-objective optimization. However, it has some shortcomings, such as low convergence accuracy, uneven Pareto front distribution, and slow convergence. A number of review papers using memetic technique to improve NSGA-II have been published. Hence, it is imperative to improve memetic NSGA-II by increasing its solving accuracy. In this paper, an improved memetic NSGA-II, called deep memetic non-sorted genetic algorithm (DM-NSGA-II), is proposed, aiming to obtain more non-dominated solutions uniformly distributed and better converged near the true Pareto-optimal front. The proposed algorithm combines the advantages of both exact and heuristic approaches. The effectiveness of DM-NSGA-II is validated using well-known instances taken from the standard literature on multi-objective knapsack problem. As will be shown, the performance of the proposed algorithm is demonstrated by comparing it with M-NSGA-II using hypervolume metric.

Date: 2021
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