Quantum Cooperative Immune Algorithm for Dynamic Optimization Problem
WU Qiu-Yi, JIAO Li-Cheng, WEI Jun, LI Yang-Yang
Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China,Institute of Intelligent Information Processing, Xidian University, Xi'an 710071
Abstract:A quantum cooperative immune algorithm is proposed for dynamic optimization problem, which is based on the synergism strategy and principles of quantum-inspired immune computing, and its global convergence is proved in theory. Individuals in a population are represented by quantum bits(qubits).In the individual's updating, the quantum rotation gate strategy and the dynamic adjusting rotation angle mechanism are applied to accelerate convergence. By using cooperative strategy, the information between the subpopulations is exchanged and the diversity of the population is improved. The stability of the proposed algorithm is strengthened to make it fit for the dynamic problem by introducing the relevance of quantum population. In the experiment, the quantum cooperative immune algorithm is tested on dynamic problem and compared with other algorithms by t test. The results indicate that the proposed algorithm has good robustness and adaptability.
[1] Bck T. On the Behavior of Evolutionary Algorithms in Dynamic Fitness Landscape // Proc of the IEEE International Conference on Evolutionary Computation. Anchorage, USA, 1998: 446-451 [2] Branke J. Evolutionary Optimization in Dynamic Environments. Dordrecht, Netherlands: Kluwer Academic Publishers, 2002 [3] Li Chengzu, Huang Mingqiu, Chen Pingxing, et al. Quantum Communication and Quantum Computation. Changsha, China: National University of Defense Technology Press, 2000 (in Chinese) (李承祖,黄明球,陈平形,等.量子通信和量子计算.长沙:国防科技大学出版社, 2000) [4] Jiao Licheng, Du Haifeng. Development and Prospect of the Artificial Immune System. Acta Electronca Sinica, 2003, 31(10): 73-80 (in Chinese) (焦李成,杜海峰.人工免疫系统的进展与展望.电子学报, 2003, 31(10): 73-80) [5] Yang Shengxiang. Non-Stationary Problem Optimization Using the Primal-Dual Genetic Algorithm // Proc of the Congress on Evolutionary Computation. Newport Beach, USA, 2003, Ⅲ: 2246-2253 [6] Li Yangyang, Jiao Licheng. Quantum-Inspired Immune Clonal Algorithm // Proc of the 4th International Conference on Artificial Immune Systems. Banff, Canada, 2005: 304 - 317 [7] Jiao Licheng, Li Yangyang, Gong Maoguo, et al. Quantum-Inspired Immune Clonal Algorithm for Global Optimization. IEEE Trans on System, Man and Cybernetics, 2008, 38(5): 1234-1253 [8] Li Yangyang, Jiao Licheng, Gou Shuiping. Quantum-Inspired Immune Clonal Algorithm for Multiuser Detection in DS-CDMA Systems // Proc of the 6th International Conference on Simulated Evolution and Learning. Hefei, China, 2006: 80-87 [9] Li Yangyang, Jiao Licheng. Quantum-Inspired Immune Clonal Algorithm // Proc of the International Conference on Artificial Immune Systems. Banff, Canada, 2005: 304-317 [10] Han K H, Kim J H. Quantum-Inspired Evolutionary Algorithms with a New Termination Criterion, H Gate, and Two-Phase Scheme. IEEE Trans on Evolution Computation, 2004, 8(2): 156-169 [11]Li Yangyang, Jiao Licheng. Quantum-Inspired Immune Clonal Algorithm for SAT Problem. Chinese Journal of Computers, 2007, 30(2): 176-183 (in Chinese) (李阳阳,焦李成.求解SAT问题的量子免疫克隆算法.计算机学报, 2007, 30(2): 176-183) [12] Wu Qiuyi, Jiao Licheng, Li Yangyang, et al. Self-Adaptive Quantum-Inspired Immune Clone Algorithm and Its Convergence Analysis. Pattern Recognition and Artificial Intelligence, 2008, 21(5): 592-597 (in Chinese) (吴秋逸,焦李成,李阳阳,等.自适应量子免疫克隆算法及其收敛性分析.模式识别与人工智能, 2008, 21(5): 592-597)