时变压缩因子粒子群算法

针对粒子群算法对全局和局部搜索平衡能力较弱的缺点，提出结合时变加速因子的粒子群算法。新算法基于压缩因子粒子群算法，利用双重压缩因子；第一个压缩因子用来调节全局和局部搜索模型；第二压缩因子利用时变的加速因子，进一步平衡全局和局部最优值对粒子种群升级的影响；通过对基本粒子群算法，压缩因子粒子群算法和混沌粒子群算法在8个标准Benchmark函数上进行三种测试，实验结果表明新算法精度较高，收敛速度较快。新算法通过时变的加速因子，较好平衡了粒子群算法的全局和局部搜索模型。

Particle swarm optimization based on time varying constrict factor

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.