Multi-Objective Optimization Using Particle Swarm Optimization Algorithm

This documentation presents a multi-objective optimization approach based on Particle Swarm Optimization (PSO) algorithm, implemented in MATLAB for effectively solving various power system optimization challenges. The Particle Swarm Optimization algorithm mimics the foraging behavior of bird flocks in nature and demonstrates exceptional performance in handling multi-objective optimization problems. The implementation includes key MATLAB functions for population initialization, velocity updating using cognitive and social components, and Pareto front maintenance for multi-objective solutions. Through parameter adjustment mechanisms such as inertia weight control and acceleration coefficient optimization, along with customized fitness function evaluation for power system constraints, this method identifies optimal solutions for power system configurations. This approach not only delivers optimized results for power systems but also provides insights into power system operational principles and optimization processes. The MATLAB code structure includes modular components for load flow calculations, constraint handling, and solution visualization, making it a comprehensive and effective solution framework for power system optimization challenges. By employing this PSO-based multi-objective optimization methodology, engineers can obtain robust solutions while understanding the underlying optimization dynamics through accessible code implementation.