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A New Multiagent Algorithm for Dynamic Continuous Optimization

Author

Listed:
  • Julien Lepagnot

    (Université de Paris 12, France)

  • Amir Nakib

    (Université de Paris 12, France)

  • Hamouche Oulhadj

    (Université de Paris 12, France)

  • Patrick Siarry

    (Université de Paris 12, France)

Abstract

Many real-world problems are dynamic and require an optimization algorithm that is able to continuously track a changing optimum over time. In this paper, a new multiagent algorithm is proposed to solve dynamic problems. This algorithm is based on multiple trajectory searches and saving the optima found to use them when a change is detected in the environment. The proposed algorithm is analyzed using the Moving Peaks Benchmark, and its performances are compared to competing dynamic optimization algorithms on several instances of this benchmark. The obtained results show the efficiency of the proposed algorithm, even in multimodal environments.

Suggested Citation

  • Julien Lepagnot & Amir Nakib & Hamouche Oulhadj & Patrick Siarry, 2010. "A New Multiagent Algorithm for Dynamic Continuous Optimization," International Journal of Applied Metaheuristic Computing (IJAMC), IGI Global, vol. 1(1), pages 16-38, January.
    • Handle: RePEc:igg:jamc00:v:1:y:2010:i:1:p:16-38
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    Cited by:

    1. Abbas El Dor & Maurice Clerc & Patrick Siarry, 2012. "A multi-swarm PSO using charged particles in a partitioned search space for continuous optimization," Computational Optimization and Applications, Springer, vol. 53(1), pages 271-295, September.

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