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    Abstract:

    Simulated annealing and multigroup parallel evolution are two helpful methods which can improve the performance of genetic algorithm.These two ideas are well combined in this paper,and a new algorithm is derived,that is the multigroup parallel genetic algorithm based on simulated annealing method.Simulation results show that this method not only quickens the computation,but also improves the convergence efficiency,thus produces more satisfactory results.

    Reference
    1  Wu Zhi-yuan,Shao Hui-he,Wu Xin-yu.Genetic annealing evolutionary algorithm.Journal of Shanghai Jiaotong University,1997,31(12):69~71 (吴志远,邵惠鹤,吴新余.遗传退火进化算法.上海交通大学学报,1997,31(12):69~71) 2  Zhou Yuan-hui,Lu Yu-chang,Shi Chun-yi.Adaptive and parallel genetic algorithm based on solving premature convergence.Journal of Tsinghua University,1998,38(3):93~95 (周远晖,陆玉昌,石纯一.基于克服过早收敛的自适应并行遗传算法.清华大学学报,1998,38(3):93~95) 3,Wang Xue-mei,Wang Yi-he.The combination of simulated annealing and genetic algorithms.Chinese Journal of Computers,1997,20(4):381~384 (王雪梅,王义和.模拟退火算法与遗传算法的结合.计算机学报,1997,20(4):381~384) 4  Kang Li-shan.Non-Numerical Parallel Algorithms ( Ⅰ)——Simulation Annealing Algorithms.Beijing: Science Press,1997.22~55 (康立山.非数值并行算法(第1册)——模拟退火算法.北京:科学出版社,1997.22~55) 5  Lin F T,Kao C Y,Hsu C C.Applying the genetic approach to simulated annealing in solving some NP-hard.IEEE Transactions on SMC,1993,23(6):1752~1767
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吴浩扬,常炳国,朱长纯,刘君华.基于模拟退火机制的多种群并行遗传算法.软件学报,2000,11(3):416-420

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History
  • Received:January 12,1999
  • Revised:April 06,1999
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