关键词: 时变, 加速因子, 压缩因子, 粒子群算法

Abstract: For the disadvantage of particle swarm optimizer is not skillful at balancing the global and local search, a new algorithm is proposed in this paper, combining the time-varying acceleration coefficients. The new algorithm, based on particle swarm optimizer with constrict factor, employs the double constrict factors. The first constrict factor aims at adjusting the model of global and local search, and the second constrict factor concentrates on further balancing the global and local optima which influence the updates of the entire swarm. By comparing with particle swarm optimizer, particle swarm optimizer with constrict factor and chaos particle swarm optimizer, which evaluated on 8 Benchmark functions, with 3 kinds of tests, the results indicate the new algorithm owns a higher accurate level, and faster convergence velocity. By using the time varying acceleration coefficients, the new algorithm can balance the global and local search better.

Key words: time varying, acceleration coefficients, constrict coefficients, particle swarm optimization