A hybrid population-based algorithm for the bi-objective quadratic multiple knapsack problem

In this paper, the bi-objective quadratic multiple knapsack problem is tackled with a hybrid population-based method. The proposed method starts by computing two reference solutions, where a specialized powerful mono-objective algorithm is used. From both reference solutions, a starting population i...

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Vydáno v:Expert systems with applications Ročník 191; s. 116238
Hlavní autoři: Aïder, Méziane, Gacem, Oussama, Hifi, Mhand
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Elsevier Ltd 01.04.2022
Elsevier BV
Elsevier
Témata:
Algorithms
Bi-objective
Computer Science
Evolutionary
Hybrid
Knapsack
Knapsack problem
Optimization
Perturbation
Population
Hybrid
Bi-objective
Knapsack
Evolutionary
Optimization
ISSN:0957-4174, 1873-6793
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Shrnutí:In this paper, the bi-objective quadratic multiple knapsack problem is tackled with a hybrid population-based method. The proposed method starts by computing two reference solutions, where a specialized powerful mono-objective algorithm is used. From both reference solutions, a starting population is built by using a series of perturbations around the solutions. Next, the so-called non-sorting genetic process is combined with a new drop/rebuild operator for generating a series of populations till converging toward an approximate Pareto front with high density. The performance of the hybrid population based algorithm (namely HBPA) is evaluated on a set of benchmark instances of the literature containing both medium and large-scale instances. Its provided results are compared to those achieved by the best methods available in the literature. Encouraging results have been obtained. •A hybrid method is designed for the multi-objective quadratic multiple knapsack.•The diversity is maintained with a learning strategy.•The method is analyzed on instances of the literature.•It reaches new dominated solutions with high approximate Pareto fronts.
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ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2021.116238