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This research presents a novel parallel model for Particle Swarm Optimization (PSO), named Multi-agent Agent-based PSO (MAPSO). The model synergizes multi-agent system principles with parallel computing techniques to enhance optimization processes. By transforming particles into autonomous intelligent agents capable of asynchronous execution and advanced learning, the swarm operates as a cohesive multi-agent system. This innovative approach seeks to improve the efficiency and performance of conventional PSO algorithms.

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A Distributed Architecture for Particle Swarm Optimization Meta-heuristic Methods Based on Multi-agent Systems

  • 会议论文

  • 发表日期：

    2025年

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  • Said Adbi,
  • Hicham Mouncif

摘要

This research presents a novel parallel model for Particle Swarm Optimization (PSO), named Multi-agent Agent-based PSO (MAPSO). The model synergizes multi-agent system principles with parallel computing techniques to enhance optimization processes. By transforming particles into autonomous intelligent agents capable of asynchronous execution and advanced learning, the swarm operates as a cohesive multi-agent system. This innovative approach seeks to improve the efficiency and performance of conventional PSO algorithms.