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Because of the complexity of super high-dimensional complex functions with the large numbers of global and local optima, the 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 adaptive strategy of inertia factor, global optimum,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 functions, and to quicken convergence speed, avoid premature problem, economize computational expenses, and obtain global optimum. We test the new algorithm and compare

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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.Keywords: particle swarm optimizer, complex optimization problem, premature convergence IntroductionAs a newly developed population-based computational intelligence algorithm, Particle Swarm Optimization (PSO) was originated as a simulation of simplified social model of birds in a flock [1]- [4]. The PSO algorithm has less parameters, easy implementation, fast convergence speed and other characteristics, is widely used in many fields,such as solving combinatorial optimization, fuzzy control, neural network training, etc. But, the PSO algorithm with other algorithms is also easy to fall into local optimumin fast convergence process, affecting the convergence precision, so how to overcome premature convergence, and improve the accuracy of convergence is always a hot and difficult problem in the research field [5]- [11].To avoidthe premature problem and speed up the convergence process, thereare many approaches suggested by researchers.According to the research results published in recent years, the improvement of PSO algorithm mainly includes adjusting algorithm parameters, the improvement of topological structure, and mixed with other algorithm, etc [6]- [12].The purpose of improvement strategiesis to balance the global search ability and local search ability of particles, so as to improve the performance of the algorithm.In this paper, we modified the traditional PSO (TPSO) algorithm with the dynamic transition strategy ofinertia factor, search space boundary andsearchvelocitythresholdbased on individual cognitionin each cycle,whichcan balance the global search ability and local search ability of particles, and has an excellent search performance to lead the search direction in early convergence stage of search process. Experimental results on several complexbenchmark functions demonstrate that this is a verypromisingway to improve the solution quality and rate of success significantly in optimizing complex engineering problems.Section 2 gives some background knowledge of the PSO algorithm. In section 3, the proposed method and the...

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