Abstract：Quantum Inspired Evolutionary Algorithm(QIEA) adopts the framework that multiple simple probabilistic models parallel explore and it can improve its exploration ability consequently by introducing more effective multi-model learning mechanism. In this paper, the idea of comprehensive learning is introduced into the learning of multiple quantum probabilistic models, and Comprehensive Learning Quantum-inspired Estimation of Distribution Algorithm(CLQEDA) is proposed. In CLQEDA, the comprehensive learning is implemented by allowing each component of model to learn from different target solutions. It makes the quantum probabilistic model possible to relatively comprehensively extract knowledge from the known better solutions, then as comprehensively as possible to describe the good area in solution space and effectively improve the performance of the algorithm on complicated optimization problems. The advance and effectiveness of CLQEDA are testified via comparison experiments on classical 0-1 knapsack problems.
谭立湘，郭立. 基于全面学习的量子分布估计算法[J]. 模式识别与人工智能, 2010, 23(3): 314-319.
TAN Li-Xiang, GUO Li. Quantum-Inspired Estimation of Distribution Algorithm Based on Comprehensive Learning. , 2010, 23(3): 314-319.
[1] Platel M D, Schliebs S, Kasabov N. Quantum-Inspired Evolutionary Algorithm: A Multimodel EDA. IEEE Trans on Evolutionary Computation, 2009, 13(6): 1218-1232 [2] Han K H, Kim J H. Quantum-Inspired Evolutionary Algorithm for a Class of Combinatorial Optimization. IEEE Trans on Evolutionary Computation, 2002, 6(6): 580-593 [3] Li Bin, Zhuang Zhenquan. Genetic Algorithm Based on the Quantum Probability Representation // Proc of the 3rd International Conference on Intelligent Data Engineering and Automated Learning. Manchester, UK, 2002: 500-505 [4] Wang Ling. Advances in Quantum 2 Inspired Evolutionary Algorithms. Control and Decision, 2008, 23(12): 1321-1326 (in Chinese) (王 凌.量子进化算法研究进展.控制与决策, 2008, 23(12): 1321-1326) [5] Li Bin, Tan Lixiang, Zou Yi, et al. Quantum Probability Coding Genetic Algorithm and Its Applications. Journal of Electronics and Information Technology, 2005, 27(5): 805-810 (in Chinese) (李 斌,谭立湘,邹 谊,等.量子概率编码遗传算法及其应用.电子与信息学报, 2005, 27(5): 805-810)
[6] Yang Junan, Li Bin, Zhuang Zhenquan. Multi-Universe Parallel Quantum Genetic Algorithm and Its Application to Blind-Source Separation // Proc of the International Conference on Neural Networks and Signal Processing. Nanjing, China, 2003: 393-398 [7] Wei Wenlong, Li Bin, Zou Yi, et al. A Multi-Objective HW-SW Co-Synthesis Algorithm Based on Quantum Probability Coding Genetic Algorithm. International Journal of Computational Intelligence and Applications, 2008, 7(2): 129-148 [8] Jang J, Han K H, Kim J H. Face Detection Using Quantum-Inspired Evolutionary Algorithm // Proc of the IEEE Congress on Evolutionary Computation. Seattle, USA, 2004: 2100-2106 [9] Li Binbin, Wang Ling. A Hybrid Quantum-Inspired Genetic Algorithm for Multi-Objective Flow Shop Scheduling. IEEE Trans on Systems, Man and Cybernetics, 2007, 37(3): 576-591 [10] Pelikan M, Goldberg D E, Lobo F G. A Survey of Optimization by Building and Using Probabilistic Models. Computational Optimization and Applications, 2002, 21(1): 5-20 [11] Harik G R, Lobo F G, Goldberg D E. The Compact Genetic Algorithm. IEEE Trans on Evolutionary Computation, 1999, 3(4): 287-297 [12] Pelikan M, Goldberg D E, Cantú-Paz E. BOA: The Bayesian Optimization Algorithm // Proc of the Genetic and Evolutionary Computation Conference. Orlando, USA, 1999, Ⅰ: 525-532 [13] de Bonet J S, Isbell C L, Viola P. MIMIC: Finding Optima by Estimating Probability Densities // Mozer M C, Petsche I, Jordan M I, eds. Advances in Neural Information Processing Systems. Cambridge, USA: MIT Press, 1996, Ⅸ: 424-430 [14] Han K H, Kim J H. Genetic Quantum Algorithm and Its Application to Combinatorial Optimization Problem // Proc of the IEEE Congress on Evolutionary Computation. La Jolla, USA, 2000, Ⅱ: 1354-1360 [15] Platel M D, Schliebs S, Kasabov N. A Versatile Quantum-Inspired Evolutionary Algorithm // Proc of the IEEE Congress on Evolutionary Computation. Singapore, Singapore, 2007: 423-430 [16] Liang J J, Qin A K, Suganthan P N, et al. Comprehensive Learning Particle Swarm Optimizer for Global Optimization of Multimodal Functions. IEEE Trans on Evolutionary Computation, 2006, 10(3): 281-295
2010, Vol. 23 
