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A New Hybrid Distributed Double Guided Genetic Swarm Algorithm for Optimization and Constraint Reasoning: Case of Max-CSPs

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  • Asma Khadhraoui

    (Hana Laboratory, ENSI-L’Ecole Nationale Des Sciences De L’informatique, University of Manouba, Tunisia)

  • Sadok Bouamama

    (Hana Laboratory, ENSI-L’Ecole Nationale Des Sciences De L’informatique, University of Manouba, Tunisia)

Abstract

In this paper the authors propose a new distributed double guided hybrid algorithm combining the particle swarm optimization (PSO) with genetic algorithms (GA) to resolve maximal constraint satisfaction problems (Max-CSPs). It consists on a multi-agent approach inspired by a centralized version of hybrid algorithm called Genetical Swarm Optimization (GSO). Their approach consists of a set of evolutionary agents dynamically created and cooperating in order to find an optimal solution. Each agent executes its own hybrid algorithm and it is able to compute its own parameters. The authors’ approach is compared to the GSO. It demonstrates its superiority. They reached these results thanks to the distribution using multi-agent systems, diversification and intensification mechanisms.

Suggested Citation

  • Asma Khadhraoui & Sadok Bouamama, 2012. "A New Hybrid Distributed Double Guided Genetic Swarm Algorithm for Optimization and Constraint Reasoning: Case of Max-CSPs," International Journal of Swarm Intelligence Research (IJSIR), IGI Global, vol. 3(2), pages 63-74, April.
    • Handle: RePEc:igg:jsir00:v:3:y:2012:i:2:p:63-74
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