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Multidimensional knapsack problem optimization using a binary particle swarm model with genetic operations

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Abstract

Particle swarm optimization is a heuristic and stochastic technique inspired by the flock of birds when looking for food. It is currently being used to solve continuous and discrete optimization problems. This paper proposes a hybrid, genetic inspired algorithm that uses random mutation/crossover operations and adds penalty functions to solve a particular case: the multidimensional knapsack problem. The algorithm implementation uses particle swarm for binary variables with a genetic operator. The particles update is performed in the following way: first using the iterative process (standard algorithm) described in the PSO algorithm and then using the best particle position (local) and the best global position to perform a random crossover/mutation with the original particle. The mutation and crossover operations specifically apply to personal and global best individuals. The obtained results are promising compared to those obtained by using the probability binary particle swarm optimization algorithm.

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Acknowledgements

The research was partially supported by Spanish Research Agency Projects TRA2013-48314-C3-2-R and SEGVAUTO TRIES (S2013/MIT-27139).

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Authors and Affiliations

  1. Dept. Sistemas Informáticos, Escuela Técnica Superior de Ingeniería de Sistemas Informáticos, Universidad Politécnica de Madrid, Crta. de Valencia Km. 7, 28031, Madrid, Spain

    Luis Fernando Mingo López & Nuria Gómez Blas

  2. Computer Science Department, Troy University, University Avenue, Troy, AL, 36081, USA

    Alberto Arteta Albert

Authors
  1. Luis Fernando Mingo López
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  2. Nuria Gómez Blas
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  3. Alberto Arteta Albert
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Correspondence to Luis Fernando Mingo López.

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This article does not contain any studies with human participants or animals performed by any of the authors.

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Communicated by V. Loia.

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Mingo López, L.F., Gómez Blas, N. & Arteta Albert, A. Multidimensional knapsack problem optimization using a binary particle swarm model with genetic operations. Soft Comput 22, 2567–2582 (2018). https://doi.org/10.1007/s00500-017-2511-0

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