一种基于混沌序列的粒子群优化算法

Particle swarm optimization algorithm based on chaotic series

提出了一种基于混沌思想的粒子群优化算法,它利用粒子群优化算法收敛速度快和混沌运动遍历性的特点,对于陷入局部极小点的粒子,引入混沌序列重新初始化,从而使惰性粒子能够跳出束缚并快速搜寻到全局最优解。对几个经典函数的测试计算表明,其在收敛速度和精度上均优于标准的PSO算法。

Based on the chaos particle swarm optimization（CPSO）, an algorithm was presented through the improvement of particle swarm optimization. Some particles trapped in local minimums are initialized again by chaotic series in order to introduce neighboring regions of local minimums in the iteration and laelp mem nreaK away from local optimum to find the globe optimal solution rapidly. The experimental results of classic functions show that the enhanced algorithm has great advantage of convergence property and robustness compared to genetic and PSO algorithm.