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Portuguese Conference on Artificial Intelligence

EPIA 2015: Progress in Artificial Intelligence pp 298–304Cite as

Memetic Algorithm for Solving the 0-1 Multidimensional Knapsack Problem

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9273))

Abstract

In this paper, we propose a memetic algorithm for the Multidimensional Knapsack Problem (MKP). First, we propose to combine a genetic algorithm with a stochastic local search (GA-SLS), then with a simulated annealing (GA-SA). The two proposed versions of our approach (GA-SLS and GA-SA) are implemented and evaluated on benchmarks to measure their performance. The experiments show that both GA-SLS and GA-SA are able to find competitive results compared to other well-known hybrid GA based approaches.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University Mhamed Bougarra of Boumerdes, Boumerdes, Algeria

    Abdellah Rezoug

  2. FEI, Department of Computer Science Beb-Ezzouar, USTHB, Algiers, Algeria

    Dalila Boughaci

  3. School of Computing, Faculty of Technology, University of Portsmouth, Portsmouth, UK

    Mohamed Badr-El-Den

Authors
  1. Abdellah Rezoug
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  2. Dalila Boughaci
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  3. Mohamed Badr-El-Den
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Corresponding author

Correspondence to Abdellah Rezoug .

Editor information

Editors and Affiliations

  1. ISEC - Coimbra Institute of Engineering, Polytechnic Institute of Coimbra, Coimbra, Portugal

    Francisco Pereira

  2. CIUSC, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal

    Penousal Machado

  3. CIUSC, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal

    Ernesto Costa

  4. CIUSC, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal

    Amílcar Cardoso

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© 2015 Springer International Publishing Switzerland

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Rezoug, A., Boughaci, D., Badr-El-Den, M. (2015). Memetic Algorithm for Solving the 0-1 Multidimensional Knapsack Problem. In: Pereira, F., Machado, P., Costa, E., Cardoso, A. (eds) Progress in Artificial Intelligence. EPIA 2015. Lecture Notes in Computer Science(), vol 9273. Springer, Cham. https://doi.org/10.1007/978-3-319-23485-4_31

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  • DOI: https://doi.org/10.1007/978-3-319-23485-4_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23484-7

  • Online ISBN: 978-3-319-23485-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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