基于双策略协同进化的QPSO算法及其应用  

Double strategies co-evolutionary quantum-behaved particle swarm optimization algorithm and its application

作　　者：何光 卢小丽 李高西 He Guang;Lu Xiaoli;Li Gaoxi(Chongqing Key Laboratory of Social Economic&Applied Statistics,Chongqing Technology&Business University,Chongqing 400067,China;School of Mathematics&Statistics,Chongqing Technology&Business University,Chongqing 400067,China;Research Center for Economy of Upper Reaches of the Yangtze River,Chongqing Technology&Business University,Chongqing 400067,China)

机构地区：[1]重庆工商大学经济社会应用统计重庆市重点实验室,重庆400067 [2]重庆工商大学数学与统计学院,重庆400067 [3]重庆工商大学长江上游经济研究中心,重庆400067

出　　处：《计算机应用研究》2023年第2期418-423,共6页Application Research of Computers

基　　金：国家自然科学基金资助项目(11901068);重庆市科委资助项目(cstc2016jcyjA0564);重庆市教委资助项目(KJQN202100815,18SKJD034);重庆工商大学科研平台开放课题(KFJJ2016008)。

摘　　要：为更好地提升量子粒子群优化算法(QPSO)的局部挖掘和全局搜索能力,提出了一种改进的QPSO算法(DSQPSO)。在改进算法中引入了双策略协同进化的思路调整粒子的位置更新公式。为充分体现个体粒子挖掘的优势和群体共同引导的特点,提出了两种吸引点构造的思路,做到个体和种群更好地融合以及信息的互通;分别考虑了最优平均位置与全局最优和粒子的历史最优之间的联系,对粒子搜索范围作出了重新定义;此外,在迭代过程中,借助随机扰动机制对全局最优位置进行调整,以保持种群的多样性。通过18个测试函数将DSQPSO算法与PSO、QPSO、RQPSO和LQPSO四种算法在收敛精度和鲁棒性方面进行对比;进而在两个具体的工程优化问题上,应用改进算法与八个智能算法进行了寻优结果比较。实验表明DSQPSO算法无论在基准测试中还是在工程应用上,其计算精度和收敛效果均有明显优势。To improve the local mining and global search ability of quantum-behaved particle swarm optimization algorithm(QPSO),this paper proposed an improved QPSO algorithm(DSQPSO).DSQPSO algorithm introduced the double strategies co-evolution to adjust the particle position update formula.Firstly,in order to fully reflect the advantage of individual exploration and the characteristic of collective guidance,this paper put forward two kinds of ideas of attraction points to achieve better integration of individuals and the swarm as well as information exchange.Secondly,it redefined the search scope of the particle through considering the relationship between the optimal average position and the global optimum and individual’s historical optimum respectively.Moreover,in the iterative process,DSQPSO used the random perturbation mechanism to adjust the global optimal position in order to help the diversity of the swarm to be preserved.Based on 18 test functions,this paper compared DSQPSO with PSO,QPSO,RQPSO and LQPSO in convergence accuracy and robustness.Furthermore,in terms of the optimization results,it compared the improved algorithm with eight intelligent algorithms on two practical engineering optimization problems.Experiments indicate that whether in benchmarking or in engineering application,DSQPSO has obvious advantages in calculation precision and convergence effect.

关 键 词：量子粒子群优化算法 协同进化 局部吸引点 最优平均位置 工程应用 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]