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A New Hybrid Binary Particle Swarm Optimization Algorithm for Multidimensional Knapsack Problem

  • Amira Gherboudj
  • Said Labed
  • Salim Chikhi
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 166)

Abstract

In this paper, we presented a New Hybrid Binary Particle Swarm Optimization (NHBPSO). This hybridization consists at combining some principles of Particle Swarm Optimization (PSO) and Crossover Operation of the Genetic Algorithm (GA). The proposed algorithm is used to solving the NP-hard combinatorial optimization problem of Multidimensional Knapsack Problem (MKP). In the aim to access the efficiency and performance of our NHBPSO algorithm we have tested it on some benchmarks from OR-Library and we have compared our results with the obtained results by the standard binary Particle Swarm Optimization with penalty function technique (PSO-P) algorithm and the quantum version (QICSA) of the new metaheuristic Cuckoo Search. The experimental results show a good and promise solution quality obtained by the proposed algorithm which outperforms the PSO-P and QICSA algorithms.

Keywords

Particle Swarm Optimization Crossover Operation Multidimensional Knapsack Problem 

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Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Computer Science Department, MISC LaboratoryMentouri UniversityConstantineAlgeria

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