PSO for Multi-Objective Optimization

Multi-objective optimization problems are highly prevalent in practical engineering applications. To address these challenges, the Particle Swarm Optimization (PSO) algorithm serves as a widely adopted modeling technique. The algorithm operates by simulating social behavior through particle swarms that explore the solution space to minimize or maximize multiple objective functions simultaneously. Implementation typically involves initializing particle positions and velocities, updating them based on personal best (pBest) and global best (gBest) solutions, and incorporating techniques like Pareto dominance for multi-objective handling. Key algorithmic components include fitness evaluation, velocity updates using inertia weights, and non-dominated sorting for maintaining solution diversity. PSO has demonstrated extensive applications across various domains including engineering design, economic modeling, and medical research. Empirical studies confirm that PSO outperforms many alternative algorithms in terms of computational efficiency and solution accuracy, leading to its widespread adoption and continuous development in multi-objective optimization scenarios.