An improved particle swarm optimization algorithm with opposition mutation

An opposition-mutation-based particle swarm optimization algorithm is presented (OMPSO) in this paper. The proposed OMPSO employs opposition-based learning algorithms, which can accelerate the learning and searching process in soft computing. The mutation threshold of OMPSO is adapted to the evolution information of the gbest, which is very useful to keep the global search ability and fast convergence of the optimization algorithm. The OMPSO has the same tuning parameters as standard particle swarm optimization algorithm (PSO) and is easily implemented in practice. At last, OMPSO is applied to several benchmark problems. Simulation results show that proposed algorithm can find global optima effectively and quickly.