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多目标量子编码遗传算法

邹谊 魏文龙 李斌 肖金超 庄镇泉

邹谊, 魏文龙, 李斌, 肖金超, 庄镇泉. 多目标量子编码遗传算法[J]. 电子与信息学报, 2007, 29(11): 2688-2692. doi: 10.3724/SP.J.1146.2006.00457
引用本文: 邹谊, 魏文龙, 李斌, 肖金超, 庄镇泉. 多目标量子编码遗传算法[J]. 电子与信息学报, 2007, 29(11): 2688-2692. doi: 10.3724/SP.J.1146.2006.00457
Zou Yi, Wei Wen-long, Li Bin, Xiao Jin-chao, Zhuang Zhen-quan. A Multi-objective Q-bit Coding Genetic Algorithm[J]. Journal of Electronics & Information Technology, 2007, 29(11): 2688-2692. doi: 10.3724/SP.J.1146.2006.00457
Citation: Zou Yi, Wei Wen-long, Li Bin, Xiao Jin-chao, Zhuang Zhen-quan. A Multi-objective Q-bit Coding Genetic Algorithm[J]. Journal of Electronics & Information Technology, 2007, 29(11): 2688-2692. doi: 10.3724/SP.J.1146.2006.00457

多目标量子编码遗传算法

doi: 10.3724/SP.J.1146.2006.00457
基金项目: 

国家自然科学基金(60401015,60572012)和安徽省自然科学基金(050420201)资助课题

A Multi-objective Q-bit Coding Genetic Algorithm

  • 摘要: 如何使算法快速收敛到真正的Pareto前沿,并保持解集在前沿分布的均匀性是多目标优化算法重点研究解决的问题。该文提出一种基于量子遗传算法的多目标优化算法,利用量子遗传算法的高效全局搜索能力,在整个解空间内快速搜索多目标函数的Pareto最优解,利用量子遗传算法维持解集多样性的特点,使搜索到的Pareto最优解在前沿均匀分布。通过求解带约束的多目标函数优化问题,对该文算法的多目标优化性能进行了考察,并与NSGAII,PAES,MOPSO和Ray-Tai-Seows算法等知名多目标优化算法进行比较,结果证明了该文算法的有效性和先进性。
  • Koski J. Multi-criterion optimization in structural design. In Attek E, Gallagher R H, and Ragsdell K M, et al. Ed. New Directions in Optimum Structural Design. New York , Wiley, 1984: 483-503.[2]Schaffer J D. Multiple Objective Optimization with Vector Evaluated Genetic Algorithms. In: Proceedings of the 1st International Conference on Genetic Algorithms, Lawrence Erlbaum Associates, Hillsdale, 1985: 93-100.Fourman M P. Compaction of symbolic layout using genetic algorithms. In Grefenstetee J J(Ed.), Proceedings of an International Conference on Genetic Algorithms and Their Applications. Pittsburgh, PA, 1985: 141-153.[3]Kursawe F. A variant of evolution strategies for vector optimization. In: Schwefel H P and Mnner R(Ed.). Parallel Problem Solving from Nature Proceedings of the first Workshop PPSN, Berlin, Springer, 1991: 193-197.[4]Fonseca C M and Fleming P J. Genetic algorithms for multi-objective optimization: formulation, discussion and generalization. Proceedings of the 5th international conference on genetic algorithms, Forrest Ed, San Mateo,CA: Morgan Kaufmann Publishers, 1993: 416-423.[5]Srinivas N and Deb Kalyanmoy. Multiobjective optimization using non-dominated sorting in Genetic algorithms[J].Evolutionary Computation.1994, 2(3):221-248[6]Deb K and Goldberg D E. An investigation of niche and species formation in genetic function optimization. In: Schaffer J D(Ed.), Proceedings of the 3rd International Conference on Genetic Algorithms, George Mason University, Fairfax, VA, USA. 1989: 42-50.[7]Joshua D K and David W C. Approximating the nondominated front using the Pareto archived evolution strategy[J].Evolutionary Computation.2000, 8(2):149-172[8]Ray T, TRai K, and Seow K C. An evolutionary algorithm for multiobjective optimization[J].Eng. Optim.2001, 33(3):399-[9]Deb K, Pratap A, Agarwal S, and Meyarivan T. A fast and elitist multi- objective genetic algorithm: NSGA-Ⅱ[J].IEEE Trans. on Evolutionary Computation.2002, 6(2):182-196[10]Carlos A Coello Coello and Maximino Salazar Lechuga. MOPSO: A proposal for multiobjective particle swarm optimization. Proceedings of the 2002 Congress on Evolutionary Computation, Hawaii, USA, 12-17 May 2002. vol.2: 1051-1056.[11]Han Kuk-Hyun and Kim Jong-Hwan. Genetic quantum algorithm and its application to combinatorial optimization problem[A]. Proceeding of the 2000 IEEE Congress on Evolutionary Computation [C]. San, Diego, 2000, 2: 1354- 1360.[12]Li Bin, et al.. Genetic algorithm based on the quantum probability representation[R]. Yin H, et al. (Ed.). Lecture Notes in Computer Science (LNCS2412), 2002: 500-505.[13]李斌,庄镇泉等. 量子概率编码遗传算法及其应用. 电子与信息学报,2005, 27(5): 808-810. Li Bin and Zhuang Zhen-quan, et al.. Quantum probability coding genetic algorithm and its applications. Journal of Electronics. Information Technology, 2005, 27(5): 805-810.
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出版历程
  • 收稿日期:  2006-04-10
  • 修回日期:  2006-11-07
  • 刊出日期:  2007-11-19

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