一种采用完全Logistic混沌的PSO-GA优化方法

为了提高粒子群优化算法的性能,提出了一种完全Logistic混沌粒子群优化与遗传算法的混合优化方法。该方法将具有伪随机性与遍历性特征的Logistic混沌应用到粒子群算法的粒子位置和速度初始化、惯性权重优化、随机常数以及局部最优解邻域点产生的全过程,并在粒子速度和位置更新后再与遗传算法相混合,进行选择和交叉操作。三种典型Benchmark函数的实验结果验证了所提方法的有效性,该方法具有更好的寻优能力与收敛速度。

Hybrid optimization method through complete Logistic chaoticparticle swarm optimization and genetic algorithm

In order to improve the optimization performance of particle swarm optimization, this paper proposed a new algorithm called complete Logistic chaotic particle swarm optimization combined with genetic algorithm. Logistic chaos search, which had the property of pseudo-randomness and ergodicity, was applied to the initialization of position and velocity of initial swarm, the optimization of inertia weight, the generation of random constant and the generation of the local optimum neighborhood point. After the particle velocity and position were updated, it embedded genetic algorithm in the complete Logistic chao-tic particle swarm optimization, to perform the operation of selection and crossover. Experimental results with three typical Benchmark functions show that the proposed algorithm is effective, and has better search property and convergence speed.