JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE) ›› 2015, Vol. 45 ›› Issue (2): 17-21.doi: 10.6040/j.issn.1672-3961.2.2014.211

Previous Articles     Next Articles

Improved quantum crossover immune clonal algorithm and its application

DAI Hongwei1,2, YANG Yu2, ZHONG Zhaoman2, LI Cunhua2   

  1. 1. Jiangsu Marine Resources Development Research Institute, Lianyungang 222005, Jiangsu, China;
    2. School of Computer Engineering, Huaihai Institute of Technology, Lianyungang 222005, Jiangsu, China
  • Received:2014-05-23 Revised:2014-10-15 Online:2015-04-20 Published:2014-05-23

Abstract: An improved quantum crossover immune clonal algorithm (IQCICA) was proposed based on two crossovers with complementary characteristics. The hybrid crossover consists of two crossovers with exploitation and exploration characteristics respectively. A user-defined parameter was used to select the crossover. The improved algorithm was used to solve the famous combinatorial optimization problems-Traveling Salesman Problems (TSP). Comparison was also performed with other algorithms. Simulation results showed that the improved algorithm had better convergence and stability, and could effectively balance the global and local search capabilities.

Key words: immune computation, traveling salesman problems, combinatorial optimization problem, clonal selection algorithm, hybrid crossover

CLC Number: 

  • TP399
[1] 朱云龙,陈瀚宁,申海. 生物启发计算:个体、群体、群落演化模型与方法[M]. 北京:清华大学出版社, 2013.
[2] LEE Z Y, PONNAMBALAM S G. Optimisation of multipass turning operations using PSO and GAAIS algorithms[J]. International Journal of Production Research, 2012, 50(22):6499-6518.
[3] SATO Y, CAMPELO F, IGARASHI H. Meander line antenna design using an adaptive genetic algorithm[J]. IEEE Transactions on Magnetics, 2013, 49(5):1889-1892.
[4] IACCA G. Distributed optimization in wireless sensor networks: an island-model framework[J]. Soft Comput, 2013(17):2257-2277.
[5] FESANGHARY M, ASADI S, GEEM Z. Design of low-emission and energy-efficient residential buildings using a multi-objective optimization algorithm[J]. Building and Environment, 2012(49):245-250.
[6] 高元海, 王淳. 无重访遗传算法及其在输电网络规划中的应用[J]. 中国电机工程学报, 2013, 33(4):110-117. GAO Yuanhai, WANG Chun. Non-revisiting genetic algorithm and its application in transmission network planning[J]. Proceedings of the CSEE, 2013, 33(4):110-117.
[7] 巩敦卫, 曾现峰, 张勇. 基于改进模拟退火算法的机器人全局路径规划[J]. 系统仿真学报, 2013, 25(3):480-483. GONG Dunwei, ZENG Xianfeng, ZHANG Yong. Global path planning method of robot based on modified simulated annealing algorithm[J]. Journal of System Simulation, 2013, 25(3):480-483.
[8] 温涛, 盛国军, 郭权, 等. 基于改进粒子群算法的Web服务组合[J]. 计算机学报, 2013, 36(5):1031-1046. WEN Tao, SHENG Guojun, GUO Quan, et al. Web service composition based on modified particle swarm optimization[J]. Chinese Journal of Computers, 2013, 36(5):1031-1046.
[9] 宋代立, 张洁. 蚁群算法求解混合流水车间分批调度问题[J]. 计算机集成制造系统, 2013, 19(7):1640-1647. SONG Daili, ZHANG Jie. Batch scheduling problem of hybrid flow shop based on ant colony algorithm[J]. Computer Integrated Manufacturing Systems, 2013, 19(7):1640-1647.
[10] 焦李成, 杜海峰, 刘芳, 等. 免疫优化—计算、学习与识别[M]. 北京:科学出版社, 2006.
[11] 郭凯, 李海芳, 王会青. 一种人工免疫的自适应谱聚类算[J]. 小微型计算机系统, 2013, 34(4):856-859. GUO Kai, LI Haifang, WANG Huiqing. An adaptive spectral clustering algorithm based on artificial immune[J]. Journal of Chinese Computer Systems, 2013, 34(4):856-859.
[12] DAI H W, YANG Y, LI C H, et al. Quantum interference crossover-based clonal selection algorithm and its application to traveling salesman problem[J]. IEICE Trans on Info & Sys, 2009, 92(1):78-85.
[13] ALI R Y. An effective hybrid immune-hill climbing optimization approach for solving design and manufacturing optimization problems in industry[J]. Journal of Materials Processing Technology, 2009(209):2773-2780.
[14] NARAYANAN A, MOORE M. Quantum-inspired genetic algorithm[C]//Proceedings of IEEE Int Conf Evolutionary Computation(1996). Nagoya, Japan: IEEE: 61-66.
[15] 李阳阳, 焦李成. 量子克隆多播路由算法[J]. 软件学报, 2007, 18(9):2063-2069. LI Yangyang, JIAO Licheng. Quantum clonal algorithm for multicast routing problem[J]. Journal of Software, 2007, 18(9):2063-2069.
[16] 戴红伟, 杨玉, 王永泉, 等. 自适应量子交叉克隆选择算法及其应用[J]. 西安交通大学学报, 2014, 48(9):6-12. DAI Hongwei, YANG Yu, WANG Yongquan, et al. Adaptive quantum crossover based clonal selection algorithm and its applications[J]. Journal of Xi'an Jiaotong University, 2014, 48(9):6-12.
[17] LIU R C, ZHANG X R, YANG N, et al. Immunodomaince based clonal selection clustering algorithm[J]. Applied Soft Computing, 2012(12):302-312.
[18] GAO S C, DAI H W, YANG G, et al. A novel clonal selection algorithm and its application to traveling salesman problems[J]. IEICE Trans Fundam, 2007(10):2318-2325.
[19] 徐京雷, 赵洪超, 刘希玉. 旅行商问题的闭环DNA算法[J]. 计算机工程与科学, 2014, 36(1):111-114. XU Jinglei, ZHAO Hongchao, LIU Xiyu. Closed circle DNA algorithm of traveling salesman problem[J]. Computer Engineering & Science, 2014, 36(1):111-114.
[20] CHANG P C, HUANG W H, TING C J. Dynamic diversity control in genetic algorithm for mining unsearched solution space in TSP problems[J]. Expert Systems with Applications, 2010(37):1863-1878.
[1] CHEN Jiajie, WANG Jinfeng. Method for solving Choquet integral model based on ant colony algorithm [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2018, 48(3): 81-87.
[2] GUO Hui-ling, WANG Shi-tong*, YAN Xiao-bo. A novel method for face recognition based on generalized rotation invariant kernel [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2012, 42(5): 71-79.
[3] ZHOU Xiao-lin,ZENG Guang-zhou . P2P-based workflow management system [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2007, 37(5): 89-94 .
Full text



No Suggested Reading articles found!