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Journal of Intelligent and Robotic Systems

, Volume 46, Issue 2, pp 129–149 | Cite as

Self-organization of Decentralized Swarm Agents Based on Modified Particle Swarm Algorithm

  • Dong H. Kim
  • Seiichi Shin
Article

Abstract

In this paper, an attempt has been made by incorporating some special features in the conventional particle swarm optimization (PSO) technique for decentralized swarm agents. The modified particle swarm algorithm (MPSA) for the self-organization of decentralized swarm agents is proposed and studied. In the MPSA, the update rule of the best agent in swarm is based on a proportional control concept and the objective value of each agent is evaluated on-line. In this scheme, each agent self-organizes to flock to the best agent in swarm and migrate to a moving target while avoiding collision between the agent and the nearest obstacle/agent. To analyze the dynamics of the MPSA, stability analysis is carried out on the basis of the eigenvalue analysis for the time-varying discrete system. Moreover, a guideline about how to tune the MPSA's parameters is proposed. The simulation results have shown that the proposed scheme effectively constructs a self-organized swarm system in the capability of flocking and migration.

Key words

decentralized swarm systems particle swarm optimization self-organization 

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Copyright information

© Springer Science+Business Media B.V. 2006

Authors and Affiliations

  1. 1.Department of Electrical EngineeringKyungnam UniversityMasanSouth Korea
  2. 2.School of Information Science and TechnologyThe University of TokyoTokyoJapan

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