Particle Swarm Optimization with Opposite Particles

摘要

The particle swarm optimization algorithm is a kind of intelligent optimization algorithm. This algorithm is prone to be fettered by the local optimization solution when the particle's velocity is small. This paper presents a novel particle swarm optimization algorithm named particle swarm optimization with opposite particles which is guaranteed to converge to the global optimization solution with probability one. And we also make the global convergence analysis. Finally, three function optimizations are simulated to show that the PSOOP is better and more efficient than the PSO with inertia weights.