Memetic Algorithm With Extended Neighborhood Search for Capacitated Arc Routing Problems

被引:158
作者
Tang, Ke [1 ]
Mei, Yi [1 ]
Yao, Xin [2 ]
机构
[1] Univ Sci & Technol China, Nat Inspired Computat & Applicat Lab, Sch Comp Sci & Technol, Hefei 230027, Peoples R China
[2] Univ Birmingham, Ctr Excellence Res Computat Intelligence & Applic, Sch Comp Sci, Birmingham B15 2TT, W Midlands, England
基金
英国工程与自然科学研究理事会; 中国国家自然科学基金;
关键词
Capacitated arc routing problem (CARP); evolutionary optimization; local search; memetic algorithm; metaheuristic search; EVOLUTIONARY ALGORITHMS; LOCAL SEARCH; OPTIMIZATION;
D O I
10.1109/TEVC.2009.2023449
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The capacitated arc routing problem (CARP) has attracted much attention during the last few years due to its wide applications in real life. Since CARP is NP-hard and exact methods are only applicable to small instances, heuristic and metaheuristic methods are widely adopted when solving CARP. In this paper, we propose a memetic algorithm, namely memetic algorithm with extended neighborhood search (MAENS), for CARP. MAENS is distinct from existing approaches in the utilization of a novel local search operator, namely Merge-Split (MS). The MS operator is capable of searching using large step sizes, and thus has the potential to search the solution space more efficiently and is less likely to be trapped in local optima. Experimental results show that MAENS is superior to a number of state-of-the-art algorithms, and the advanced performance of MAENS is mainly due to the MS operator. The application of the MS operator is not limited to MAENS. It can be easily generalized to other approaches.
引用
收藏
页码:1151 / 1166
页数:16
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