量子免疫克隆多目标优化算法

李阳阳; 焦李成

doi:10.3724/SP.J.1146.2006.01709

量子免疫克隆多目标优化算法

doi: 10.3724/SP.J.1146.2006.01709

基金项目:

国家自然科学基金(60703108)，国家863计划(2006AA01Z107)，国家 973项目 (2006CB705700)和陕西省自然科学基金(2007F32)资助课题

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出版历程
- 收稿日期: 2006-11-02
- 修回日期: 2007-05-15
- 刊出日期: 2008-06-19

Quantum-Inspired Immune Clonal Multiobjective Optimization Algorithm

摘要

摘要: 该文基于免疫系统的免疫优势概念和抗体克隆选择学说,采用量子位编码,提出了一种量子免疫克隆多目标优化算法,并对算法进行了理论分析;与RWGA、SPEA和MISA等算法的比较表明,该算法对低维多目标优化问题更有效。
- 人工免疫系统; 量子位编码; 多目标优化
Abstract: Based on the concept of immunodominance,antibody clonal selection theory and quantum bit strategy, a Quantum-inspired Immune Clonal Multiobjective Optimization Algorithm(QICMOA)is proposed.The QICMOA is compared with RWGA,SPEA and MISA in solving low-dimensional problems.The statistical results show that QICMOA has a good performance in converging to true Pareto-optimal fronts with a good distribution.

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参考文献(1)

Rosenberg R S. Simulation of genetic populations withbiochemical properties. [Ph.D. dissertation], University ofMichigan, 1967.[2]Schaffer J D. Multiple objective optimization with vectorevaluated genetic algorithms. In the Proceedings of theInternational Conference on Genetic Algorithms and TheirApplications, Pittsburgh, PA, 1985: 93-100.[3]Hajela P and Lin C Y. Genetic search strategies inmulticriterion optimal design[J].Structural Optimization.1992,4(2):99-107[4]Fonseca C M and Fleming P J. Multiobjective optimizationand multiple constraint handling with evolutionaryalgorithms-part I: A unified formulation[J].IEEE Trans. onSystems, Man, and Cybernetics.1998, 28(1):26-37[5]Srinivas N and Deb K. Multiobjective optimization usingnondominated sorting in genetic algorithms[J].Evol. Comput.1994, 2(3):221-248[6]Horn J, Nafpliotis N, and Goldberg D E. A niched paretogenetic algorithm for multiobjective optimization. In Proc.1st IEEE Conf Evolutionary Computation, IEEE WorldCongr. Computational Computation, Piscataway, NJ, 1994, 1:82-87.[7]Zitzler E and Thiele L. Multiobjective evolutionaryalgorithms: A comparative case study and the strengthpareto approach[J].IEEE Trans. on Evolutionary Computation.1999, 3(4):257-271[8]Coello C A C. A comprehensive survey of evolutionarybasedmultiobjective optimization techniques. Knowledge andInformation Systems, 1999, 1(3): 269-308.[9]Coello C A C and Cortes N C. An approach to solvemultiobjective optimization problems based on an artificialimmune system. First Int. Conf on Artificial Immune Systems,Inglaterra, 2002: 212-221.[10]Deb K, Pratap A, Agarwal S, and Meyarivan T. A fast andelitist multiobjective genetic algorithm: NSGA-II[J].IEEETrans. on Evolutionary Computation.2002, 6(2):182-197[11]Li Y Y and Jiao L C. Quantum-inspired immune clonalalgorithm. In Proceedings of the 4th International Conferenceon Artificial Immune Systems, Banff, Alberta, Canada, 2005:304-317.[12]Jordan A N. Topics in quantum chaos. [Ph.D. dissertation],University of California Santa Barbara, 2002.[13]吕金虎，鲁君安，陈士华. 混沌时间序列分析及其应用. 武汉：武汉大学出版社，2002.[14]Ishibuchi H and Murata T. A multiobjective genetic localsearch algorithm and its application to flowshop scheduling[J].IEEE Trans. on System, Man and Cybernetics.1998, 28(3):392-403

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