Multi-Agent Based Particle Swarm Optimization Algorithm

This program is implemented using MATLAB. Building upon traditional basic particle swarm optimization (PSO), we integrated multi-agent system principles to develop an enhanced multi-agent based particle swarm optimization (MAB-PSO) algorithm. The implementation features agent-based coordination mechanisms where individual particles operate as autonomous agents with localized decision-making capabilities, while maintaining global optimization objectives through interactive communication protocols. We applied this advanced algorithm to solve electric power load distribution problems, implementing specific constraint handling mechanisms and fitness functions tailored for power system optimization. The comparative analysis includes performance metrics such as convergence speed, solution quality, and computational efficiency against standard PSO implementations. Through comprehensive testing and validation, we obtained detailed insights into the algorithm's practical applications and effectiveness, particularly noting improved exploration-exploitation balance and better handling of complex constraint environments in power system optimization scenarios.