Applying area extension PSO in robotic swarm

Adham Atyabi, Somnuk Phon-Amnuaisuk, Chin Ho

    Research output: Contribution to journalArticlepeer-review

    17 Citations (Scopus)


    Particle Swarm optimization (PSO) is a search method inspired from the social behaviors of animals. PSO has been found to outperform other methods in various tasks. Area Extended PSO (AEPSO) is an enhanced version of PSO that achieves better performance by balancing its essential intelligent behaviours more intelligently. AEPSO incorporates knowledge with the aim of choosing proper behaviors in each situation. This study provides a comparison between the variations of Basic PSO and AEPSO aiming to address dynamic and time dependent constraint problems in simulated robotic search. The problem is set up in a multi-robot learning scenario. The scenario is based on the use of a team of simulated robots (hereafter referred to as agents) who participate in survivor rescuing missions. The experiments are classified into three simulations. At first, agents employ variations of basic PSO as their decision maker and movement controllers. The first simulation investigates the impacts of swarm size, parameter adjustment, and population density on agents' performance. Later, AEPSO is employed to improve the performance of the swarm in the same simulations. The final simulation investigates the feasibility of AEPSO in time-dependent, dynamic and uncertain environments. As shown by the results, AEPSO achieves an appreciable level of performance in dynamic, time-dependence and uncertain simulated environments and outperforms the variations of basic PSO, Linear Search and Random Search used in the simulations.

    Original languageEnglish
    Pages (from-to)253-285
    Number of pages33
    Issue number3-4
    Publication statusPublished - Jun 2010


    • Area extension PSO
    • Dynamic
    • Particle swarm optimization
    • Robotic swarm
    • Time dependent
    • Uncertainty


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