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An Adaptive Particle Swarm Optimization Algorithm for Distributed Search and Collective Cleanup in Complex Environment

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  • Yi Cai
  • Zhutian Chen
  • Jun Li
  • Qing Li
  • Huaqing Min

Abstract

Distributed coordination is critical for a multirobot system in collective cleanup task under a dynamic environment. In traditional methods, robots easily drop into premature convergence. In this paper, we propose a Swarm Intelligence based algorithm to reduce the expectation time for searching targets and removing. We modify the traditional PSO algorithm with a random factor to tackle premature convergence problem, and it can achieve a significant improvement in multi-robot system. It performs well even in a obstacle environment. The proposed method has been implemented on self-developed simulator for searching task. The simulation results demonstrate the feasibility, robustness, and scalability of our proposed method compared to previous methods.

Suggested Citation

  • Yi Cai & Zhutian Chen & Jun Li & Qing Li & Huaqing Min, 2013. "An Adaptive Particle Swarm Optimization Algorithm for Distributed Search and Collective Cleanup in Complex Environment," International Journal of Distributed Sensor Networks, , vol. 9(12), pages 560579-5605, December.
    • Handle: RePEc:sae:intdis:v:9:y:2013:i:12:p:560579
    DOI: 10.1155/2013/560579
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