一种基于PSO-ABC的全局优化算法

针对粒子群算法和蜂群算法在寻优中存在的一些早熟和收敛速精度不高等问题,论文分别时粒子算法和蜂群算法的更新策略以及更新公式进行了改进,利用改进的粒子群算法和改进的蜂群算法同时对一个粒子位置进行部分算术更新的方法,提出了一种新混合的优化算法.并将其在12个多极值基准函数进行全局最优化测试,实验结果表明,笔者提出的混合优化算法收敛的速度和收敛精度大大提高了,其性大大优于改进的粒子群算法（CLPSO算法）和人工蜂群算法,对于高、低维复杂函数的优化均适用.

A Hybrid Optimization Algorithm based on Particle Swarm Optimization Algorithm and Artificial Bee Colony Algorithm

Concerning the problems of premature and low convergence accuracy for the particle swarm algorithm in search of optimization, update strategies and formulas of the particle algorithm and bee colony algorithm have been made improvements in this paper, respectively. And a new mixed optimization algo- rithm was proposed based on updated arithmetic method to a particle position using improved particle swarm optimization and bee colony algorithm. Through global optimization tests in 12 multiple maximum bench- mark functions, the experimental results show the convergence accuracy of the mixed optimization algorithm has improved greatly and the rate of convergence become faster, and the performance is better than improved Comprehensive Learning Particle Swarm Optimization （CLPSO） Algorithm and artificial bee colony algo- rithm, which can be applied in the optimization for high and low dimensional complicated functions.