[1]刘振,刘文彪,鲁华杰.一种量子行为磷虾算法及其仿真分析[J].郑州大学学报(工学版),2018,39(06):43-49.[doi:10.13705/j.issn.1671-6833.2018.06.017]
　Liu Zhen,Liu Wenbiao,Lu Huajie.A Quantum Behaved Krill Herd Algorithm and Its Simulation Analysis[J].Journal of Zhengzhou University (Engineering Science),2018,39(06):43-49.[doi:10.13705/j.issn.1671-6833.2018.06.017]
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一种量子行为磷虾算法及其仿真分析()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
39
期数:
2018年06期
页码:
43-49
栏目:
出版日期:
2018-10-24

文章信息/Info

Title:
A Quantum Behaved Krill Herd Algorithm and Its Simulation Analysis
作者:
刘振; 刘文彪; 鲁华杰
海军航空大学岸防兵学院
Author(s):
Liu Zhen;Liu Wenbiao;Lu Huajie
Naval Aviation University Coastal Defense Academy
关键词:
磷虾算法; 协同进化; 量子; 多种群; 势阱
Keywords:
Krill algorithm; co-evolution; quantum; multiple populations; potential well
DOI:
10.13705/j.issn.1671-6833.2018.06.017
文献标志码:
A
摘要:
针对基本磷虾算法收敛效率低下，容易收敛到局部极值的缺点，基于协同进化和量子计算基本理论，提出一种量子行为磷虾算法，称为协同进化量子磷虾（cooperative evolution quantum krill herd algorithm, CEQKHA）算法。该算法将磷虾种群划分为主种群和辅种群，各种群能够独立进化，并实现优良个体的交换。利用量子进化行为方式更新磷虾个体位置，引进delta势阱，将粒子势阱中心设置为最优个体位置，获取磷虾进化后的位置，并分别将主种群和辅种群个体采用不同的位置更新方式，提高种群勘探和开采的能力.对所提出的算法进行了收敛性分析，证明了所提出算法的收敛性能。最后利用基准函数进行了仿真对比分析，经仿真验证，所提出的CEQKHA能得到更优解，具备良好的优化性能。
Abstract:
Aiming at the low efficiency and easy trap into local optimum for the basic krill herd algorithm, a new quantum behave krill herd algorithm was proposed. It was named as cooperative evolution quantum krill herd algorithm (CEQKHA) based on cooperative evolution and quantum computation. The population could be divided into two parts, such as main population and sub-population, which could evolve independently and exchange fine individuals. The position of krill herd could be updated by using quantum activity. The best position of krill herd could be set as the center of potential well of delta potential well, and the position of krill herd in the different population can evolve in different way. The convergence of the algorithm was also deduced. Simulation of benchmark functions proved that CEQKHA could get better results and perform well than other algorithms

相似文献/References:

[1]梁静,刘睿,瞿博阳,等.进化算法在大规模优化问题中的应用综述[J].郑州大学学报(工学版),2018,39(03):15.[doi:10.13705/j.issn.1671-6833.2017.06.016]
　Liang Jing,Liu Rui,Qu Boyang,et al.A Survey of Evolutionary Algorithms for  Large Scale Optimization Problem[J].Journal of Zhengzhou University (Engineering Science),2018,39(06):15.[doi:10.13705/j.issn.1671-6833.2017.06.016]

更新日期/Last Update: 2018-10-26