Artificial bee colony algorithm with generating neighbourhood solution for large scale coloured traveling salesman problem

被引:33
作者
Dong, Xueshi [1 ,2 ]
Lin, Qing [1 ]
Xu, Min [3 ]
Cai, Yongle [2 ]
机构
[1] Qingdao Univ, Coll Comp Sci & Technol, Qingdao, Shandong, Peoples R China
[2] Wuhan Univ, Comp Sch, Wuhan, Hubei, Peoples R China
[3] Chang Jiang Waterway Inst Planning & Design, Wuhan, Hubei, Peoples R China
基金
中国国家自然科学基金;
关键词
simulated annealing; search problems; travelling salesman problems; genetic algorithms; artificial bee colony algorithm; large-scale CTSP; combination optimisation problems; multiple travelling salesman problems; real-world planning problems; multimachine engineering system; population-based algorithms; ABC algorithms; large scale coloured travelling salesman problem; genetic algorithm; simulated annealing algorithm; generating neighbourhood solution;
D O I
10.1049/iet-its.2018.5359
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Coloured travelling salesman problem (CTSP) is an extended model of multiple travelling salesman problems (MTSPs), as one kind of problem in combination optimisation problems, which has been applied to many real-world planning problems such as multi-machine engineering system (MES). Population-based algorithms such as genetic algorithm (GA) and simulated annealing (SA) algorithm are only used to solve small- or medium-scale cases in which the city number is <2000. Furthermore, in term of the convergence speed and solution quality, their performances have further improvement space. Since many real-world problems can be modelled by large-scale CTSP, it is necessary to study better algorithms to solve large-scale CTSP. Since artificial bee colony algorithm (ABC) can show good performance in solving combination optimisation problems, and therefore this study applied improved ABC algorithms to solve large-scale CTSP. The modified ABC algorithms use generating neighbourhood solution (GNS) to improve ABC for this problem, two limitation and optimisation conditions are applied into GNS during the process of reinserting cities. The extensive experiments verify that the improved algorithms can demonstrate better solution quality than the compared algorithms for large-scale CTSP.
引用
收藏
页码:1483 / 1491
页数:9
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