Tabu Assisted Local Search for the Minimum Load Coloring Problem

Ye, Ansheng; Zhang, Zhiqiang; Zhou, Xiaoqing; Miao, Fang

doi:10.1166/jctn.2014.3664

Cited by 4 publications

(6 citation statements)

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“…All algorithms were programmed in C++, and run on a PC with Intel Pentium(R) G630 processor 2.70 GHz and 4 GB memory under Windows 7 (64 bits), and the test graphs adopted were the benchmark DIMACS proposed in [5]. We compared the search results by using memetic algorithm, simulated annealing algorithm, and greedy algorithm.…”

Section: Resultsmentioning

confidence: 99%

“…We compared the search results by using memetic algorithm, simulated annealing algorithm, and greedy algorithm. Then, the results of memetic algorithm were compared with those obtained from using artificial bee colony algorithm [4], tabu search algorithm [5] and variable neighborhood search algorithm [5].…”

Section: Resultsmentioning

confidence: 99%

“…We evaluate the performance of memetic algorithm on 59 well-known graphs from benchmark DIMACS coloring competitions. The computational results show that the search ability of memetic algorithm is better than those of simulated annealing algorithm, greedy algorithm, artificial bee colony algorithm [4] and variable neighborhood search algorithm [5]. In particular, it improves the best known results of 16 graphs in known literature algorithms.…”

Section: Introductionmentioning

confidence: 89%

“…Heuristic algorithms use rules based on previous experience to solve a combinatorial optimization problem at the cost of acceptable time and space, and, at the same time, comparatively better results can be obtained. The heuristic algorithms used here include an artificial bee colony algorithm [4], a tabu search algorithm [5] and a variable neighborhood search algorithm [5] to solve MLCP.…”

Section: Related Workmentioning

confidence: 99%

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An Efficient Memetic Algorithm for the Minimum Load Coloring Problem

Zhang

Qiao

2019

Mathematics

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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 an improved K-OPT local search and an evolutionary operation. Furthermore, a data splitting operation is executed to expand the data amount of global search, and a disturbance operation is employed to improve the search ability of the algorithm. Experiments are carried out on the benchmark DIMACS to compare the searching results from memetic algorithm and the proposed algorithms. The experimental results show that a greater number of best results for the graphs can be found by the memetic algorithm, which can improve the best known results of MLCP.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 89%

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

An Efficient Memetic Algorithm for the Minimum Load Coloring Problem

Zhang

Qiao

2019

Mathematics

Self Cite

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“…Se basa en la idea que es mayor la información que brindan los errores que los aciertos, TS ha establecido récords en la búsqueda de mejores soluciones a los problemas de planificación de la producción y la programación, asignación de recursos, diseño de redes, enrutamiento, análisis financiero, las telecomunicaciones, la planificación de la cartera, gestión de la cadena de suministro, el modelado basado en agentes, el diseño de procesos de negocio, la previsión, el aprendizaje automático, minería de datos, etc. Muchos trabajos que utilizan este algoritmo hacen visible la robustez del método frente a alta carga de tráfico en escenarios dinámicos para redes ópticas WDM (Glover 1983;Glover 1989;Ye et al, 2014, Rodríguez et al, 2014. …”

Section: Tabu Search (Ts)unclassified

Solución Metaheurística para el Problema de Enrutamiento (RWA) en Redes de Ópticas de Multiplexación por División de Longitud de Onda (WDM)

2017

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ResumenEl artículo muestra los resultados de una nueva metaheurística basada en el algoritmo Snake One que se ha denominado Snake Three. Se basa en la utilización de los enlaces hasta la lograr su congestión máxima. De esta forma se logra cierto orden de uso de los recursos dejando libre el resto de enlaces para satisfacer la demanda entrante o futura. La red utilizada en la simulación es la NSFNET de los Estados Unidos bajo parametrización similar a otras simulaciones, logrando estresar el sistema hasta los 180 Erlangs. Los resultados muestran una mejora en el indicador utilización de la red, aunque el indicador probabilidad de bloqueo no muestra mejora. La metaheurística Snake Three es la última de la serie Snake y ha demostrado su eficacia para poder ser utilizada en otros escenarios de multiplexación. Palabras clave: algoritmos heurísticos; algoritmos snake; redes ópticas; RWA; WDM Metaheuristic Solution for the Routing and Wavelength Assignment (RWA) Problem in Wavelength-Division Multiplexing (WDM) Optical Networks AbstractThe article shows the results of a new metaheuristic based on the Snake One algorithm that has been called Snake Three. It is based on the use of links until achieving maximum congestion. In this way, some order in the use of resources is achieved by leaving free the rest of the links to satisfy the incoming or future demand. The network used in the simulation is the NSFNET, of the United States, under parameterization similar to other simulations, achieving to stress the system up to 180 Erlangs. The results show improvement in the indicator utilization of the network, while the indicator probability of blocking does not show improvement. The Snake Three metaheuristic is the latest in the Snake series and has proved its effectiveness to be used in other multiplexing scenarios.

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