Adaptive Particle Swarm Optimization Algorithm

Application Background: Particle Swarm Optimization (PSO) is an optimization algorithm inspired by natural phenomena such as bird flocking and fish schooling. It simulates particle movement and cooperation within a search space to find optimal solutions. PSO has been widely applied across various fields including engineering optimization, machine learning, and image processing. Its simplicity and efficiency make it a popular choice for solving complex optimization problems. Key Technologies: For each particle i = 1, 2, ...: Initialize particle position with uniformly distributed random vector: x_i ~ U(blo, bup), where blo and bup represent lower and upper search space bounds. Initialize particle's best-known position to its initial position: p_i ← x_i. Update swarm's best-known position if f(p_i) < f(g): g ← p_i. Initialize particle velocity: v_i ~ U(-|bup-blo|, |bup-blo|). The algorithm iteratively updates velocities and positions using social and cognitive components until meeting termination criteria.