融合禁忌搜索和惯性权重动态调整的二进制粒子群优化算法  

Binary Particle Swarm Optimization Algorithm Combining Tabu Search and Dynamic Adjustment of Inertial Weights

作　　者：卓荣庆[1] 孙歆[1] 陈阳[1] ZHUO Rongqing;SUN Xin;CHEN Yang(Communication University of Zhejiang,Hangzhou Zhejiang 310018,China)

机构地区：[1]浙江传媒学院,浙江杭州1310018

出　　处：《信息与电脑》2023年第12期57-60,共4页Information & Computer

基　　金：教育部人文社会科学研究一般项目“基于情景感知和领域本体的适应性学习路径推荐机制研究”(项目编号:18YJC880159);教育部人文社会科学研究一般项目“基于动态用户画像的图书馆个性化资源推荐机制研究”(项目编号:20YJA870004)。

摘　　要：传统的二进制粒子群算法在优化过程中容易出现收敛速度慢和容易陷入局部最优的问题,因此提出了一种融合禁忌搜索和惯性权重动态调整的二进制粒子群优化算法。该算法不仅综合了粒子群优化算法的快速性、随机性、全局收敛性的优点,而且能够进行禁忌搜索以实现局部寻优。通过在4个基准测试函数上进行实验,实验结果表明该算法在逃离局部最优解方面具有更好的能力,提高了算法的收敛性能。The traditional binary particle swarm optimization algorithm is prone to slow convergence speed and easy to fall into local optimum in the optimization process.To solve the above problems,this paper proposes a binary particle swarm optimization algorithm that combines tabu search and inertial weight dynamic adjustment.This algorithm not only combines the advantages of fastness,randomness and global convergence of the particle swarm optimization algorithm,but also performs tabu search to achieve local optimization.Through experiments on four benchmark functions,the experimental results show that the algorithm has a better ability to escape from local optimal solutions,and also improves the convergence performance of the algorithm.

关 键 词：禁忌搜索 动态惯性权重 二进制粒子群算法 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]