An Improved Unordered Pair Bat Algorithm for Solving the Symmetrical Traveling Salesman Problem

IF 1.8 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Zhang Nan, Zhimin Lv, Qiao Shen, Li Ting
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引用次数: 1

Abstract

Abstract Bat algorithm is an effective swarm intelligence optimization algorithm which is widely used to solve continuous optimization problems. But it still has some limitations in search process and can’t solve discrete optimization problems directly. Therefore, this paper introduces an unordered pair and proposes an unordered pair bat algorithm (UPBA) to make it more suitable for solving symmetric discrete traveling salesman problems. To verify the effectiveness of this method, the algorithm has been tested on 23 symmetric benchmarks and compared its performance with other algorithms. The results have shown that the proposed UPBA outperforms all the other alternatives significantly in most cases.
求解对称旅行商问题的改进无序对蝙蝠算法
Bat算法是一种有效的群体智能优化算法,广泛用于解决连续优化问题。但它在搜索过程中仍有一定的局限性,不能直接解决离散优化问题。为此,本文引入了无序对,并提出了一种无序对蝙蝠算法(UPBA),使其更适合求解对称离散旅行商问题。为了验证该方法的有效性,在23个对称基准上对该算法进行了测试,并与其他算法进行了性能比较。结果表明,在大多数情况下,所提出的UPBA明显优于所有其他替代方案。
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来源期刊
Foundations of Computing and Decision Sciences
Foundations of Computing and Decision Sciences COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
2.20
自引率
9.10%
发文量
16
审稿时长
29 weeks
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