混沌映射的禁忌同步随机学习因子粒子群算法  

Tabu Synchronization Stochastic Learning Factor Particle Swarm Optimization Based on Chaotic Mapping

作　　者：苏攀 张伟[1] SU Pan;ZHANG Wei(School of Optoelectronic Information and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China)

机构地区：[1]上海理工大学光电信息与计算机工程学院,上海200093

出　　处：《小型微型计算机系统》2022年第8期1675-1680,共6页Journal of Chinese Computer Systems

基　　金：国家自然科学基金项目(11502145,61074087,61703277)资助.

摘　　要：针对粒子群算法初始化个体质量参差不齐,算法后期容易早熟,陷入局部最优值以及后期搜索精度不高、收敛速度缓慢的缺点,本文提出一种基于混沌映射的禁忌同步随机学习因子粒子群算法.利用Logistic映射对算法的粒子种群进行初始化,提高种群个体质量;在算法进入后期搜索寻优时,引入禁忌搜索策略,利用其良好突跳能力,跳出局部最优值,提高算法的全局搜索能力;最后将传统的学习因子通过几个测试函数进行迭代寻优,选取寻优能力突出的区间构建同步随机学习因子,平衡粒子的个体经验和群体经验.将改进的粒子群算法与另外几个智能算法在测试函数上寻优对比验证,仿真实验证明,改进的粒子群算法在寻优能力、收敛速度、搜索精度以及算法的稳定性等性能上,与另外3个智能算法相比都有显著提升.Aiming at the shortcomings of particle swarm optimization algorithm(PSO),such as uneven quality of initialized individuals,prematurity of the algorithm in the late stage,falling into local optimal value,low search accuracy and slow convergence speed,this paper proposes a tabu synchronous random learning factor PSO based on chaotic mapping.Logistic mapping was used to initialize the particle population of the algorithm to improve the individual quality of the population.When the algorithm enters the later stage,tabu search strategy is introduced,and its good jump ability is used to jump out of the local optimal value to improve the global search ability of the algorithm.Finally,the traditional learning factors were iteratively optimized through several test functions,and the interval with outstanding optimization ability was selected to construct the synchronous random learning factors to balance the individual experience and group experience of the particles.The improved particle swarm optimization algorithm was compared with other intelligent algorithms on the test function,and the simulation experiment proved that the improved particle swarm optimization algorithm has significantly improved compared with the other three intelligent algorithms in the performance of optimization ability,convergence speed,search accuracy and algorithm stability.

关 键 词：粒子群算法 混沌运动 禁忌搜索算法 学习因子 测试函数 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]