An Improved Cuckoo Search Algorithm With Stud Crossover for Chinese TSP Problem

Anbang Wang, Lihong Guo, Yuan Chen, Junjie Wang, Luo Liu and Yuanzhang Song
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Anbang Wang: Changchun Institute of Optics, Fine Mechanics, and Physics, Chinese Academy of Sciences, China
Lihong Guo: Changchun Institute of Optics, Fine Mechanics, and Physics, Chinese Academy of Sciences, China
Yuan Chen: Changchun Institute of Optics, Fine Mechanics, and Physics, Chinese Academy of Sciences, China
Junjie Wang: Changchun Institute of Optics, Fine Mechanics, and Physics, Chinese Academy of Sciences, China
Luo Liu: Changchun Institute of Optics, Fine Mechanics, and Physics, Chinese Academy of Sciences, China
Yuanzhang Song: Changchun Institute of Optics, Fine Mechanics, and Physics, Chinese Academy of Sciences, China

International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 2021, vol. 15, issue 4, 1-26

Abstract: The travelling salesman problem (TSP) is an NP-hard problem in combinatorial optimization. It has assumed significance in operations research and theoretical computer science. The problem was first formulated in 1930 and since then, has been one of the most extensively studied problems in optimization. In fact, it is used as a benchmark for many optimization methods. This paper represents a new method to addressing TSP using an improved version of cuckoo search (CS) with Stud (SCS) crossover operator. In SCS method, similar to genetic operators used in various metaheuristic algorithms, a Stud crossover operator that is originated from classical Stud genetic algorithm, is introduced into the CS with the aim of improving its effectiveness and reliability while dealing with TSP. Various test functions had been used to test this approach, and used subsequently to find the shortest path for Chinese TSP (CTSP). Experimental results presented clearly demonstrates SCS as a viable and attractive addition to the portfolio of swarm intelligence techniques.

Date: 2021
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