2019
DOI: 10.3390/math7050475
|View full text |Cite
|
Sign up to set email alerts
|

An Efficient Memetic Algorithm for the Minimum Load Coloring Problem

Abstract: Given a graph G with n vertices and l edges, the load distribution of a coloring q: V → {red, blue} is defined as dq = (rq, bq), in which rq is the number of edges with at least one end-vertex colored red and bq is the number of edges with at least one end-vertex colored blue. The minimum load coloring problem (MLCP) is to find a coloring q such that the maximum load, lq = 1/l × max{rq, bq}, is minimized. This problem has been proved to be NP-complete. This paper proposes a memetic algorithm for MLCP based on … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
1
0

Year Published

2019
2019
2022
2022

Publication Types

Select...
3

Relationship

0
3

Authors

Journals

citations
Cited by 3 publications
(1 citation statement)
references
References 23 publications
(35 reference statements)
0
1
0
Order By: Relevance
“…Many different types of heuristics and metaheuristics have been developed, including the genetic algorithm (GA) (Holland [16], Kampelis, Sifakis, Kolokotsa, Gobakis, Kalaitzakis, Isidori and Cristalli [17]; Lara-Ramirez, Garcia-Capulin, Estudillo-Ayala, Avina-Cervantes, Sanchez-Yanez, and Rostro-Gonzalez [18]), the memetic algorithm (Zhang, Li, Qiao, and Wang [19]; Cheng and Lai [20]), ant colony optimization (Ebadinezhad, Dereboylu, and Ever [21]; Jiang, Xu, and Chen [22], iterated local search (Gao, Zhu, Liu, Meng, and Zhang [23]; Hu, Liu, Wu, Li, Zhou, and Wang [24]), adaptive large neighborhood search (Liu,Du,Zhang,Li,and Shi [25]; Praseeratasang, Pitakaso, Sethanan, and Kaewman [26]; Theeraviriya, Pitakaso, Sillapasa, and Kaewman [27]), particle swam optimization (PSO) (Kennedy, Eberhart, and Shi [28], Xu and Ren [29]), and DE (Zhang,Feng,and Lin [30]; Dechampai, Tanwanichkul, Sethanan, and Pitakaso, [31]).…”
Section: Introductionmentioning
confidence: 99%
“…Many different types of heuristics and metaheuristics have been developed, including the genetic algorithm (GA) (Holland [16], Kampelis, Sifakis, Kolokotsa, Gobakis, Kalaitzakis, Isidori and Cristalli [17]; Lara-Ramirez, Garcia-Capulin, Estudillo-Ayala, Avina-Cervantes, Sanchez-Yanez, and Rostro-Gonzalez [18]), the memetic algorithm (Zhang, Li, Qiao, and Wang [19]; Cheng and Lai [20]), ant colony optimization (Ebadinezhad, Dereboylu, and Ever [21]; Jiang, Xu, and Chen [22], iterated local search (Gao, Zhu, Liu, Meng, and Zhang [23]; Hu, Liu, Wu, Li, Zhou, and Wang [24]), adaptive large neighborhood search (Liu,Du,Zhang,Li,and Shi [25]; Praseeratasang, Pitakaso, Sethanan, and Kaewman [26]; Theeraviriya, Pitakaso, Sillapasa, and Kaewman [27]), particle swam optimization (PSO) (Kennedy, Eberhart, and Shi [28], Xu and Ren [29]), and DE (Zhang,Feng,and Lin [30]; Dechampai, Tanwanichkul, Sethanan, and Pitakaso, [31]).…”
Section: Introductionmentioning
confidence: 99%