Complex Optimization Problems Using Highly Efficient Particle Swarm Optimizer

Many engineering problems are the complex optimization problems with the large numbers of global andlocal optima. Due to its complexity, general particle swarm optimization method inclines towards stagnation phenomena in the later stage of evolution, which leads to premature convergence. Therefore, a highly efficient particle swarm optimizer is proposed in this paper, which employ the dynamic transitionstrategy ofinertia factor, search space boundary andsearchvelocitythresholdbased on individual cognitionin each cycle to plan large-scale space global search and refined local search as a whole according to the fitness change of swarm in optimization process of the engineering problems, and to improve convergence precision, avoid premature problem, economize computational expenses, and obtain global optimum. Several complex benchmark functions are used to testify the new algorithm and the results showed clearly the revised algorithm can rapidly converge at high quality solutions.