Research Progress on Intelligent Optimization Guidance Approaches Using Knowledge
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摘要: 为了提高智能优化方法的优化性能,国内外学者通过知识来加强对优化过程的引导.对基于 知识的智能优化引导方法进行了综述:一方面通过传统人工智能手段来实现对智能优化方法的引导; 另一方面通过特定知识模型来实现对智能优化方法的引导.从前期优化过程中挖掘有用知识,采用知识来引导后续优化过程,极大地提高了智能优化方法的优化性能.Abstract: To improve the performance of intelligent optimization approaches, many researchers have used knowledge to strengthen the optimization process guidance. The intelligent optimization guidance using knowledge is reviewed in this work. The intelligent optimization guidance is normally executed via artificial intelligence approaches and special knowledge models. Also, some researchers have proposed algorithms which have a double layer evolution mechanism. These improved approaches can discover some knowledge from the previous iterations, then use the discoverd knowledge to guide the subsequent iterations.
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[1] Holland J H. Adaptation in Natural and Artificial Systems. Cambridge: The MIT Press, 1975[2] Cavaretta M J. Using a culture algorithm to control genetic operators. In: Proceedings of the 3rd Annual Conference on Evolutionary Programming. San Diego, California: World Scientific Publishing, 1994. 24-26[3] Sebag M, Ravise C, Schoenauer M. Controlling evolution by means of machine learning. In: Proceedings of the 5th Annual Conference on Evolutionary Programming. San Diego, USA: The MIT Press, 1996. 57-66[4] Gu Hui, Gong Yu-Chang, Zhao Zhen-Xi. A knowledge model based genetic algorithm. Computer Engineering, 2000, 26(5): 19-20(顾慧, 龚育昌, 赵振西. 基于知识模型的改进遗传算法. 计算机工程, 2000, 26(5): 19-20)[5] Fan Lei, Ruan Huai-Zhong, Jiao Yu, Luo Wen-Jian, Cao Xian-Bin. Conduct evolution using induction learning. Journal of University of Science and Technology of China, 2001, 31(5): 565-634(范磊, 阮怀忠, 焦誉, 罗文坚, 曹先彬. 用归纳学习引导进化. 中国科学技术大学学报, 2001, 31(5): 565-634)[6] Cao Xian-Bin, Xu Kai, Zhang Jie, Wang Xu-Fa. Ecological evolution model guided by life period. Journal of Software, 2000, 11(6): 823-828(曹先彬, 许凯, 章洁, 王煦法. 基于生命期引导的生态进化模型. 软件学报, 2000, 11(6): 823-828)[7] Cen Yu-Sen, Xiong Fang-Min, Zeng Bi-Qing. Grouping particle swarm optimization algorithms based on knowledge space. Computer Engineering and Design, 2010, 31(7): 1562-1565(岑宇森, 熊芳敏, 曾碧卿. 基于知识空间的分组式粒子群算法. 计算机工程与设计, 2010, 31(7): 1562-1565)[8] Li Ya-Nan, Zhang Li-Zi, Shu Juan, Leng Jiao-Lin, Yang Yi-Han. Application of expert knowledge adopted genetic algorithm to optimization of reactive power planning. Power System Technology, 2001, 25(7): 14-17(李亚男, 张粒子, 舒隽, 冷教麟, 杨以涵. 结合专家知识的遗传算法在无功规划优化中的应用. 电网技术, 2001, 25(7): 14-17)[9] Chai Xiao-Long. Ant swarm planning algorithm optimized by domain knowledge. Computer Engineering and Applications, 2010, 46(14): 17-19(柴啸龙. 领域知识优化的蚁群规划算法. 计算机工程与应用, 2010, 46(14): 17-19)[10] Chou F D. An experienced learning genetic algorithm to solve the single machine total weighted tardiness scheduling problem. Expert Systems with Applications, 2009, 36(2): 3857-3865[11] Sim K M, Guo Y Y, Shi B Y. BLGAN: Bayesian learning and genetic algorithm for supporting negotiation with incomplete information. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 2009, 39(1): 198-211[12] Liu F, Zeng G Z. Study of genetic algorithm with reinforcement learning to solve the TSP. Expert Systems with Applications, 2009, 36(3): 6995-7001[13] Ho W H, Tsai J T, Lin B T, Chou J H. Adaptive network-based fuzzy inference system for prediction of surface roughness in end milling process using hybrid Taguchi-genetic learning algorithm. Expert Systems with Applications, 2009, 36(2): 3216-3222[14] Hong Y, Kwong S. To combine steady-state genetic algorithm and ensemble learning for data clustering. Pattern Recognition Letters, 2008, 29(9): 1416-1423[15] Chi H M, Ersoy O K, Moskowitz H, Ward J. Modeling and optimizing a vendor managed replenishment system using machine learning and genetic algorithms. European Journal of Operational Research, 2007, 180(1): 174-193[16] Ho N B, Tay J C, Lai E M K. An effective architecture for learning and evolving flexible job-shop schedules. European Journal of Operational Research, 2007, 179(2): 316-333[17] Reynolds R G. An introduction to cultural algorithms. In: Proceedings of the 3rd Annual Conference on Evolutionary Programming. San Diego, USA: World Scientific Publishing, 1994. 131-139[18] Louis S J, McDonnell J. Learning with case-injected genetic algorithms. IEEE Transactions on Evolutionary Computation, 2004, 8(4): 316-328[19] Kamall K, Jiang L J, Yen J, Wang K W. Using Q-learning and genetic algorithms to improve the efficiency of weight adjustments for optimal control and design problems. Journal of Computing and Information Science in Engineering, 2007, 7(4): 302-308[20] Juang C F, Lu C M. Ant colony optimization incorporated with fuzzy Q-learning for reinforcement fuzzy control. IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans, 2009, 39(3): 597-608[21] Michalski R S. Learnable evolution model: evolution process guided by machine learning. Machine Learning, 2000, 38(1): 9-40[22] Chung C J, Reynolds R G. A testbed for solving optimization problems using cultural algorithm. In: Proceedings of the 5th Annual Conference on Evolutionary Programming. San Diego, USA: The MIT Press, 1996. 225-236[23] Branke J. Memory enhanced evolutionary algorithms for changing optimization problems. In: Proceedings of the Congress on Evolutionary Computation. Washington D.C., USA: IEEE, 1999. 1875-1882[24] Gantovnik V B, Anderson-Cook C M, Gurdal Z, Watson L T. A genetic algorithm with memory for mixed discrete-continuous design optimization. Computers and Structures, 2000, 81(20): 2003-2009[25] Gantovnik V B, Gurdal Z, Watson L T. A genetic algorithm with memory for optimal design of laminated sandwich composite panels. Composite Structures, 2002, 58(4): 513-520[26] Louis S, Li G. Augmenting genetic algorithms with memory to solve traveling salesman problems. In: Proceedings of the Joint Conference on Information Sciences. North Carolina, USA: Duke University Press, 1997. 108-111[27] Yang S X. Memory-based immigrants for genetic algorithms in dynamic environments. In: Proceedings of the Conference on Genetic and Evolutionary Computation. New York, USA: ACM, 2005. 1115-1122[28] Yang S X. Genetic algorithms with memory and elitism-based immigrants in dynamic environments. Evolutionary Computation, 2008, 16(3): 385-416[29] Su Miao, Qian Hai, Wang Xu-Fa. Immune memory-based ant colony algorithm for weapon-target assignment solution. Computer Engineering, 2004, 34(4): 215-217(苏淼, 钱海, 王煦法. 基于免疫记忆的蚁群算法的WTA问题求解. 计算机工程, 2004, 34(4): 215-217)[30] Acan A. An external memory implementation in ant colony optimization. In: Proceedings of the 4th International Workshop on Ant Colony Optimization and Swarm Intelligence. Brussels, Belgium: Springer, 2004. 73-82[31] Acan A. An external partial permutations memory for ant colony optimization. In: Proceedings of the 5th European Conference on Evolutionary Computation in Combinatorial Optimization. Lausanne, Switzerland: Springer, 2005. 1-11[32] Shamsipur M, Zare-Shahabadi V, Hemmateenejad B, Akhond M. An efficient variable selection method based on the use of external memory in ant colony optimization. Application to QSAR/QSPR studies. Analytica Chimica Acta, 2009, 646(1-2): 39-46[33] Louis S J, Li G. Case injected genetic algorithms for traveling salesman problems. Information Sciences, 2000, 122(2-4): 201-225[34] Rasheed K, Hirsh H. Using case-based learning to improve genetic algorithm based design optimization. In: Proceedings of the 7th International Conference on Genetic Algorithms. East Lansing, USA: Morgan Kaufmann, 1997. 513-520[35] Babbar-Sebens M, Minsker B. A case-based micro interactive genetic algorithm (CBMIGA) for interactive learning and search: methodology and application to groundwater monitoring design. Environmental Modeling and Software, 2010, 25(10): 1176-1187[36] Coletti M. A preliminary study of learnable evolution methodology implemented with C4.5. In: Proceedings of the Congress on Evolutionary Computation. Honolulu, USA: IEEE, 2002. 588-593[37] Wojtusiak J. Initial Study on handling constrained optimization problems in learnable evolution model. In: Proceedings of the Graduate Student Workshop at Genetic and Evolutionary Computation Conference. Seattle, USA: IEEE, 2006. 1-7[38] Kaufman K A, Michalski R S. Applying learnable evolution model to heat exchanger design. In: Proceedings of the 17th National Conference on Artificial Intelligence and 12th Conference on Innovative Applications of Artificial Intelligence. Austin, USA: The MIT Press, 2000. 1014-1019[39] Jourdan L, Corne D, Savic D A, Walters G A. Preliminary investigation of the `learnable evolution model' for faster/better multiobjective water systems design. In: Proceedings of the 3rd International Conference on Evolutionary Multi-Criterion Optimization. Guanajuato, Mexico: Springer, 2005. 841-855[40] Domanski P A, Yashar D, Kaufman K A, Michalski R S. An optimized design of finned-tube evaporators using the learnable evolution model. International Journal of Heating, Ventilating, Air-Conditioning and Refrigerating Research, 2004, 10(2): 201-211[41] Wojtusiak J, Michalski R S. The LEM3 implementation of learnable evolution model and its testing on complex function optimization problems. In: Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation. Seattle, USA: ACM, 2006. 1281-1288[42] Michalski R S, Wojtusiak J, Kaufman K A. Progress Report on Learnable Evolution Model. Fairfax, VA: George Mason University, 2007[43] Michalski R S, Wojtusiak J, Kaufman K A. Intelligent optimization via learnable evolution model. In: Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence. Arlington, USA: IEEE, 2006. 332-335[44] Wojtusiak J. The LEM3 system for multitype evolutionary optimization. Computing and Informatics, 2009, 28(3): 225-236[45] Xing Li-Ning, Chen Ying-Wu. Research on the Knowledge-based Intelligent Approaches. Changsha: National University of Defense Technology Press, 2010(邢立宁, 陈英武. 知识型智能优化方法研究. 长沙: 国防科学技术大学出版社, 2010)[46] Xing L N, Chen Y W, Yang K W. A novel mutation operator based on the immunity operation. European Journal of Operational Research, 2009, 197(2): 830-833[47] Xing L N, Chen Y W, Yang K W, Hou F, Shen X S, Cai H P. A hybrid approach combining an improved genetic algorithm and optimization strategies for the asymmetric traveling salesman problem. Engineering Applications of Artificial Intelligence, 2008, 21(8): 1370-1380[48] Xing L N, Rohlfshagen P, Chen Y W, Yao X. An evolutionary approach to the multidepot capacitated arc routing problem. IEEE Transactions on Evolutionary Computation, 2010, 14(3): 356-374[49] Xing L N, Rohlfshagen P, Chen Y W, Yao X. A hybrid ant colony optimization algorithm for the extended capacitated arc routing problems. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 2011, 41(4): 1110-1123[50] Xing L N, Chen Y W, Wang P, Zhao Q S, Xiong J. A knowledge-based ant colony optimization for flexible job shop scheduling problems. Applied Soft Computing, 2010, 10(3): 888-896[51] Xing L N, Chen Y W, Yang K W. Multi-population interactive coevolutionary algorithm for flexible job shop scheduling problems. Computational Optimization and Applications, 2011, 48(1): 139-155[52] Chai Tian-You, Ding Jin-Liang, Wang Hong, Su Chun-Li. Hybrid intelligent optimal control method for operation of complex industrial processes. Acta Automatica Sinica, 2008, 34(5): 505-515(柴天佑, 丁进良, 王宏, 苏春翌. 复杂工业过程运行的混合智能优化控制方法. 自动化学报, 2008, 34(5): 505-515)[53] Yan Ai-Jun, Chai Tian-You, Yue Heng. Multivariable intelligent optimizing control approach for shaft furnace roasting process. Acta Automatica Sinica, 2006, 32(4): 636-640(严爱军, 柴天佑, 岳恒. 竖炉焙烧过程的多变量智能优化控制. 自动化学报, 2006, 32(4): 636-640)[54] Xing Li-Ning, Chen Ying-Wu. Mission planning of satellite ground station system based on the hybrid ant colony optimization. Acta Automatica Sinica, 2008, 34(4): 414-418(邢立宁, 陈英武. 基于混合蚁群优化的卫星地面站系统任务调度方法. 自动化学报, 2008, 34(4): 414-418)
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