Decomposing large-scale capacitated arc routing problems using a random route grouping method

Yi MEI; Xiaodong LI; Xin YAO

doi:10.1109/CEC.2013.6557678

Decomposing large-scale capacitated arc routing problems using a random route grouping method

Yi MEI, Xiaodong LI, Xin YAO

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

18 Citations (Scopus)

Abstract

In this paper, a simple but effective Random Route Grouping (RRG) scheme is developed to decompose the Large-Scale Capacitated Arc Routing Problem (LSCARP). A theoretical analysis is given to show that the decomposition is guaranteed to be improved by RRG along with the improvement of the best-sofar solution during the search process. Then, RRG is combined with a cooperative co-evolution model to solve LSCARP. The experimental results on the EGL-G LSCARP set showed that given the same computational budget, the proposed approach obtained much better results than its counterpart without using decomposition. © 2013 IEEE.

Original language	English
Title of host publication	2013 IEEE Congress on Evolutionary Computation, CEC 2013
Pages	1013-1020
Number of pages	8
DOIs	https://doi.org/10.1109/CEC.2013.6557678
Publication status	Published - Jun 2013
Externally published	Yes

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title = "Decomposing large-scale capacitated arc routing problems using a random route grouping method",

abstract = "In this paper, a simple but effective Random Route Grouping (RRG) scheme is developed to decompose the Large-Scale Capacitated Arc Routing Problem (LSCARP). A theoretical analysis is given to show that the decomposition is guaranteed to be improved by RRG along with the improvement of the best-sofar solution during the search process. Then, RRG is combined with a cooperative co-evolution model to solve LSCARP. The experimental results on the EGL-G LSCARP set showed that given the same computational budget, the proposed approach obtained much better results than its counterpart without using decomposition. {\textcopyright} 2013 IEEE.",

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AU - LI, Xiaodong

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N2 - In this paper, a simple but effective Random Route Grouping (RRG) scheme is developed to decompose the Large-Scale Capacitated Arc Routing Problem (LSCARP). A theoretical analysis is given to show that the decomposition is guaranteed to be improved by RRG along with the improvement of the best-sofar solution during the search process. Then, RRG is combined with a cooperative co-evolution model to solve LSCARP. The experimental results on the EGL-G LSCARP set showed that given the same computational budget, the proposed approach obtained much better results than its counterpart without using decomposition. © 2013 IEEE.

AB - In this paper, a simple but effective Random Route Grouping (RRG) scheme is developed to decompose the Large-Scale Capacitated Arc Routing Problem (LSCARP). A theoretical analysis is given to show that the decomposition is guaranteed to be improved by RRG along with the improvement of the best-sofar solution during the search process. Then, RRG is combined with a cooperative co-evolution model to solve LSCARP. The experimental results on the EGL-G LSCARP set showed that given the same computational budget, the proposed approach obtained much better results than its counterpart without using decomposition. © 2013 IEEE.

UR - https://www.scopus.com/pages/publications/84881567888

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DO - 10.1109/CEC.2013.6557678

M3 - Conference paper (refereed)

SN - 9781479904549

SP - 1013

EP - 1020

BT - 2013 IEEE Congress on Evolutionary Computation, CEC 2013

ER -

Decomposing large-scale capacitated arc routing problems using a random route grouping method

Abstract

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