混合并行遗传算法求解TSP问题
A hybrid parallel genetic algorithm and its application to TSP
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摘要: 该文应用多种群遗传并行进化的思想,对不同种群基于不同的遗传策略,如变异概率,不同的变异算子等来搜索变量空间,并利用种群间迁移算子来进行遗传信息交流,以解决经典遗传算法的收敛到局部最优值问题,对于TSP(Traveling Salesman Problem)进行了求解,仿真结果表明,该文算法的收敛性能优于经典遗传算法。Abstract: This paper applies a multiple population Genetic Algorithm (GA) to solving the TSP (Traveling Salesman Problem). Different populations apply different mutation factors to achieve different search objects. The transition factor among the groups is used to solve the premature convergence problem under some circumstances. It accelerates search process in state space. The experimental results show that this algorithm has great advantage of convergence property over canonical genetic algorithm.
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D.B. Fogel, Evolutionary Computation [M], New York, IEEE Press, 1995, 33-99. ［2］C.K. Mohan, Selective crossover: Towards fitter offspring, Tech. Report SU-EECS TR 97-1,Dept. of EECS, Syracuse University, 1997.[2]B. Yoon, D. J. Holmes, Efficient genetic algorithms for training layered feed forward neural networks, Information Sciences, 1994, 76(1/2), 67-85.[3]J.H. Holland, Adaptation in Natural and Artificial Systems, Michigan University Press, 1975,12-73.[4]玄光男,程润伟,遗传算法与工程设计,北京,科学出版社,2000,1-145.[5]G. Reinelt, TSPLIB; ftp://softlib.rice.edu/pub/tsplib/tsplib/tsplib.tar, 1995. -
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