混合并行遺傳算法求解TSP問題
A hybrid parallel genetic algorithm and its application to TSP
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摘要: 該文應(yīng)用多種群遺傳并行進化的思想,對不同種群基于不同的遺傳策略,如變異概率,不同的變異算子等來搜索變量空間,并利用種群間遷移算子來進行遺傳信息交流,以解決經(jīng)典遺傳算法的收斂到局部最優(yōu)值問題,對于TSP(Traveling Salesman Problem)進行了求解,仿真結(jié)果表明,該文算法的收斂性能優(yōu)于經(jīng)典遺傳算法。
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關(guān)鍵詞:
- 遺傳算法; 多種群; 遷移算子; 收斂; TSP
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. -
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,程潤偉,遺傳算法與工程設(shè)計,北京,科學(xué)出版社,2000,1-145.[5]G. Reinelt, TSPLIB; ftp://softlib.rice.edu/pub/tsplib/tsplib/tsplib.tar, 1995. -
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