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改进的混合遗传模拟退火算法及其在组合优化中的应用研究 被引量:7

Research on Improved Mixed Genetic-SimulatedAnnealin Algorithms and Their Applicationin Combinatorial Optimization
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摘要 本文分析了遗传算法和模拟退火算法的优缺点,提出了一种混合遗传模拟退火MGASA算法,对其进行了优化操作,并将该算法应用于组合优化中TSP问题的解决。经实验验证,MGASA算法优于普通的GA和SA算法。 This paper analyses the advantages and disadvantages of genetic algorithm and simulated annealing, puts forwarda mixed genetic algorithm and simulated annealing called MGASA, and optimizes its implementation. This paperalso gives the implementation and result of using the method of MGASA to solve the TSP problem. From the resultwe conclude that the MGASA method is superior to GA and SA algorithms.
出处 《现代计算机》 2004年第1期14-16,41,共4页 Modern Computer
关键词 组合优化问题 遗传算法 模拟退火算法 MGASA算法 混合算法 旅行商问题 Genetic Algorithm Simulated Annealing MGASA Algorithm Combinatorial Optimization TSP problem
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参考文献5

  • 1Garrison W Greenwood, Ajay Gupta. Scheduling Task in Mulfiprocessor System using Evolutionary Strategies. The International Joint Conference on Neural Networks, Nagoya,Japan, 1993.
  • 2Fogel D B. System Identification Through Simulated Evolution: a Machine Learning Approach to Modeling. Ginn Press,1991.
  • 3Grefenstelle JJ, Gopal R, Rosmaita B, et al. Genetic Algorithms for the Traveling Salesman, International Conf of Genetic Algorithm and Their Applications, Pittsburgh, 1985.
  • 4Hopfield JJ. Tank D W. Neural Computation of Decisions in Optimization Problems. Biological Cybem, 1985.
  • 5Goldberg D E. Genetic Algorithms in Search,Optimize and Machine Learning. New York:Addiso Wes-ley,1993.

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