Single-Objective Particle Swarm Optimization Algorithm

A commonly used algorithm for parameter optimization is the Single-Objective Particle Swarm Optimization (PSO) algorithm. This algorithm is applicable to various optimization problems and can efficiently search for optimal solutions within the search space. The single-objective PSO mimics the collective behavior of bird flocks, continuously adjusting parameters through iterative updates of particle positions and velocities to optimize the objective function. The algorithm operates by maintaining a population of particles where each particle represents a potential solution, with its position updated based on personal best (pBest) and global best (gBest) values. Key implementation components include: 1) Initialization of particle positions and velocities within defined bounds, 2) Fitness evaluation using the objective function, 3) Velocity update equation incorporating inertia weight and acceleration coefficients, and 4) Position update with boundary handling. The algorithm's principles are straightforward and easy to understand, making it simple to implement while demonstrating strong performance in solving real-world optimization problems. Typical applications include parameter tuning, function optimization, and engineering design optimization where the algorithm efficiently balances exploration and exploitation through its social learning mechanism.