Hybrid Metaheuristics based on MOEA/D for 0/1 multiobjective knapsack problems: A comparative study

Abstract

Hybrid Metaheuristics aim to incorporate and combine different metaheuristics with each other to enhance the search capabilities. It can improve both of intensification and diversification toward the preferred solutions and concentrates the search efforts to investigate the promising regions in the search space. In this paper, a comparative study was developed to study the effect of the hybridization of different metaheuristics within MOEA/D framework. We study four proposals of hybridization, the first proposal is to combine adaptive discrete differential evolution operator with MOEA/D. The second one is to combine the path-Relinking operator with MOEA/D. the third and the fourth proposals combine both of them in MOEA/D. The comparative study uses a set of MOKSP instances commonly used in the literature to investigate the hybridization effects as well as a set of quality assessment indicators. The experimental results indicate that the proposals are highly competitive for most test instances and can be considered as viable alternatives.

Neuroevolution for solving multiobjective knapsack problems

“…Although various schemes have been investigated ( Mumford, 2003 ), a binary solution representation is most frequently used for handling MOKPs, in part because it is easy to implement and understand. Some algorithms that succeeded in continuous domain were extended specifically to deal with binary search spaces, including a discrete differential evolution ( Kafafy, Bounekkar, & Bonnevay, 2012 ) and a binary cuckoo search ( Layeb, Lahouesna, & Kireche, 2013 ).…”

Moea/D-Go for Fragmented Antenna Design

“…Population and neighborhood size (N, T in [14]) of these three algorithms, minimal hammming distance ε, and ratio to apply path relink γ in MOEA/D-PR [17] are listed in Table 1. In this paper, both weighted sum and Tchebycheff decomposition approaches are used.…”

“…In MOEA/D, a MOP is decomposed into several scalar subproblems which can be optimized simultaneously. By utilizing the information of solutions of neighborhood subproblems, MOEA/D has less computational cost, which has been proved by several numerical tests [14][15][16][17].…”

“…In Section 2, we propose MOEA/D-GO, and outline its features and its differences from original MOEA/D. Section 3 compares MOEA/D-GO with original MOEA/D and MOEA/D-PR (MOEA/D combined with Path-Relinking operator) [17]. In Section 4, a fragmented antenna design is performed by using our developed MOEA/D-GO.…”