Pheromone-based crossover operator of genetic algorithm for the traveling salesman problem
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摘要: 提出了求解TSP問題的一種新的基于信息素的遺傳交叉算子,并對算子構造子個體的過程進行了實驗分析.在生成子個體時,基于信息素的遺傳交叉算子不僅能夠利用包括邊長度和鄰接關系在內的局部信息,還可以利用以信息素形式保存的全局信息.在純遺傳算法框架內,利用TSP基準算例對所提出的交叉算子的性能進行了實驗測試.結果表明,該算子在精度和收斂速度上均優于其他知名的交叉算子.
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關鍵詞:
- 信息素 /
- 交叉算子 /
- 遺傳算法 /
- 旅行商問題(TSP)
Abstract: A new pheromone-based crossover operator of genetic algorithm for the traveling salesman problem was proposed, and the working process of the operator was analyzed when constructing offspring. When constructing offspring, the proposed operator utilizes both local and global irdormation. The local information includes edge lengths and adjacency relations, while the global information is stored as pheromone trails. The proposed operator was tested in a pure genetic algorithm framwork using the TSP benchmark instances. Experimental results show its better performance in both of speed and accuracy than other well known crossover operators.-
Key words:
- pheromone /
- crossover operator /
- genetic algorithm /
- traveling salesman problem(TSP)
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