Integration of Multi-Agent Learning, Coordination Strategies, and Particle Swarm Optimization
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Resource Overview
By integrating multi-agent learning, coordination strategies, and particle swarm optimization, we propose a novel distribution network reconfiguration method based on multi-agent particle swarm optimization. This approach leverages the topological structure of particle swarm algorithms to construct a multi-agent system architecture, where each particle functions as an agent that competes and cooperates with neighboring agents. The methodology enables faster and more precise convergence to global optimal solutions. The update rules for particles reduce the generation of infeasible solutions and enhance algorithm efficiency. Experimental results demonstrate that this method achieves high search efficiency and optimization performance. The implementation involves designing agent interactions, defining fitness functions, and optimizing velocity update mechanisms.
Detailed Documentation
In this study, we integrate multi-agent learning, coordination strategies, and particle swarm optimization to propose a novel distribution network reconfiguration method. This method is based on multi-agent particle swarm optimization, utilizing the topological structure of particle swarm algorithms to construct a multi-agent system architecture. In this multi-agent system, each particle acts as an agent that competes and cooperates with neighboring agents. The approach enables faster and more precise convergence to the global optimum while minimizing the generation of infeasible solutions through refined particle update rules, thereby improving algorithm efficiency. Our experimental results confirm that this method exhibits high search efficiency and optimization performance. Through this research, we contribute significantly to the field of distribution network reconfiguration and provide valuable insights for future studies. The implementation includes agent behavior modeling, neighborhood communication protocols, and fitness evaluation functions to ensure robust optimization.
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