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Effect of learning strategies in an evolutionary method: the case of the bi-objective quadratic multiple knapsack problem

  • S.I. : 2019 India Intl. Congress on Computational Intelligence
  • Published:
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Abstract

In this paper, we solve the bi-objective quadratic multiple knapsack problem, an NP-Hard combinatorial optimization problem, with a cooperative evolutionary method. The proposed method starts by generating a first approximate Pareto front by using the \(\varepsilon \)-constraint operator-based approach, that is, the first stage of the evolutionary method. The second stage is based upon an iterative procedure, where the non-dominated sorting genetic algorithm is employed for generating a series of populations. In order to avoid a premature convergence, at each step of the iterative procedure, learning strategies are added: (i) the fusion operator and (ii) the \(\varepsilon \)-constraint operator. These learning strategies are introduced for maintaining the diversity of the series of populations and so trying to avoid premature convergence and stagnations on local optima. The performance of the proposed evolutionary method with learning strategies 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. New results have been obtained.

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Author information

Authors and Affiliations

  1. LaROMaD, Fac. Maths, USTHB BP 32 El Alia, 16111, Bab Ezzouar, Algiers, Algeria

    Méziane Aïder & Oussama Gacem

  2. EPROAD UR 4669, Université de Picardie Jules Verne, 7 rue du Moulin Neuf, 80000, Amiens, France

    Mhand Hifi

Authors
  1. Méziane Aïder
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  2. Oussama Gacem
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  3. Mhand Hifi
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Contributions

—In this paper, all authors are listed in alphabetical order of their last name, according to the Operational Research domain in France, and all the work was supervised by Pr Mhand Hifi. —Mhand Hifi proposed the main idea related to learning strategies. —Oussama Gacem (Phd student under the supervision of Pr Mhand Hifi) coded the first version of the algorithm which is based upon the above idea. He tested the final version of the code on a set of benchmark instances of the literature. —Méziane Aider is the second co-advisor of Oussama Gacem. —Mhand Hifi investigated the sensitivity analysis and extended the experimental part with a statistical analysis. —All authors discussed the results and Mhand Hifi provided the final version of the manuscript.

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Correspondence to Mhand Hifi.

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All authors are listed in alphabetical order of their last name, according to the OR domain in France.

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Aïder, M., Gacem, O. & Hifi, M. Effect of learning strategies in an evolutionary method: the case of the bi-objective quadratic multiple knapsack problem. Neural Comput & Applic 35, 1183–1209 (2023). https://doi.org/10.1007/s00521-022-07555-0

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