An improved particle swarm algorithm and its application in grinding process optimization

An improved particle swarm optimization algorithm with opposition mutation (OMPSO) is presented and applied to choose satisfied parameter of grinding process. 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 global best, 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. Results of numerical examples indicate that the proposed algorithm is an effective method for grinding process optimization problem.