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Many  engineering problems are the complex optimization problems with the large numbers of global and local optima, due to its complexity, general particle swarm optimization methods are slow speed on convergence and easy to be trapped in local optima. In this paper, a highly efficient particle swarm optimizer is proposed, which employ the dynamic transition strategy of inertia factor, search space and velocity in 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 quicken convergence speed, 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.

Complex Optimization problems Using Highly Efficient Particle Swarm Optimizer