A Multi-agent System-Based Parallel Model for Particle Swarm Meta-heuristic Optimization Methods
摘要
The aim of this research is to introduce an innovative parallel model for the Particle Swarm Optimization (PSO) algorithm grounded in multi-agent collaboration, named Multi-agent Agent-based PSO (MAPSO). This model integrates the principles of multi-agent systems with the concept of parallelism within the domain of optimization. The approach upgrades particles to intelligent agents, enhancing their autonomy and intelligence. By providing particles with increased autonomy, asynchronous execution, and enhanced learning capabilities, the swarm is elevated to the level of a multi-agent system.