Reinforcement learning based local search for grouping problems: A case study on graph coloring

Zhou, Yangming; Hao, Jin‐Kao; Duval, Béatrice

doi:10.1016/j.eswa.2016.07.047

Cited by 65 publications

(21 citation statements)

References 43 publications

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“…Unless, DSJC125.5 where the proposed algorithm does not succeed to surpass it. Concerning (Zhou, 2016) the results show that the RLS algorithm exceed the proposed approach in DSJC125 and Miles500 graphs.…”

Section: Resultsmentioning

confidence: 93%

A Novel Greedy Genetic Algorithm to Solve Combinatorial Optimization Problem

Basmassi

Benameur

Chentoufi

2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. In this paper, a modified genetic algorithm based on greedy sequential algorithm is presented to solve combinatorial optimization problem. The algorithm proposed here is a hybrid of heuristic and computational intelligence algorithm where greedy sequential algorithm is used as operator inside genetic algorithm like crossover and mutation. The greedy sequential function is used to correct non realizable solution after crossover and mutation which contribute to increase the rate of convergence and upgrade the population by improving the quality of chromosomes toward the chromatic number. Experiments on a set of 6 well-known DIMACS benchmark instances of graph coloring problem to test this approach show that the proposed algorithm achieves competitive results in comparison with three states of art algorithms in terms of either success rate and solution quality.

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Section: Resultsmentioning

confidence: 93%

A Novel Greedy Genetic Algorithm to Solve Combinatorial Optimization Problem

Basmassi

Benameur

Chentoufi

2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Indeed, the results obtained by this type of approach are for the moment far from the results obtained by state of the art algorithms on graph coloring problems such as hybrid algorithms [21,23,25,28] and simulated annealing algorithms [35]. We can mention however new works which are trying to pair efficient local search algorithms and machine learning techniques [10,40,39] with promising results for graph coloring problems.…”

Section: Introductionmentioning

confidence: 93%

A deep learning guided memetic framework for graph coloring problems

Goudet¹,

Grelier²,

Hao³

2021

Preprint

Self Cite

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Given an undirected graph G = (V, E) with a set of vertices V and a set of edges E, a graph coloring problem involves finding a partition of the vertices into different independent sets. In this paper we present a new framework which combines a deep neural network with the best tools of "classical" metaheuristics for graph coloring. The proposed algorithm is evaluated on the weighted graph coloring problem and computational results show that the proposed approach allows to obtain new upper bounds for medium and large graphs. A study of the contribution of deep learning in the algorithm highlights that it is possible to learn relevant patterns useful to obtain better solutions to this problem.

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“…At each step, the reinforcement-learning algorithm selects actions from the optional-action array according to the action-selection strategy. There are generally four action-selection strategies that were detailed by Zhou et al [30]: random selection, greedy selection; roulette selection based on the values of experience matrix Q; and hybrid selection, which mixes random-selection and greedy-selection strategies. The choice of action-selection strategy is very important for the reinforcement-learning algorithm.…”

Section: 24mentioning

confidence: 99%

Tabu search guided by reinforcement learning for the max-mean dispersion problem

Nijimbere¹,

Zhao²,

Gu³

et al. 2021

JIMO

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We present an effective hybrid metaheuristic of integrating reinforcement learning with a tabu-search (RLTS) algorithm for solving the maxmean dispersion problem. The innovative element is to design using a knowledge strategy from the Q-learning mechanism to locate promising regions when the tabu search is stuck in a local optimum. Computational experiments on extensive benchmarks show that the RLTS performs much better than stateof-the-art algorithms in the literature. From a total of 100 benchmark instances, in 60 of them, which ranged from 500 to 1,000, our proposed algorithm matched the currently best lower bounds for all instances. For the remaining 40 instances, the algorithm matched or outperformed. Furthermore, additional support was applied to present the effectiveness of the combined RL technique. The analysis sheds light on the effectiveness of the proposed RLTS algorithm.

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Reinforcement learning based local search for grouping problems: A case study on graph coloring

Cited by 65 publications

References 43 publications

A Novel Greedy Genetic Algorithm to Solve Combinatorial Optimization Problem

A Novel Greedy Genetic Algorithm to Solve Combinatorial Optimization Problem

A deep learning guided memetic framework for graph coloring problems

Tabu search guided by reinforcement learning for the max-mean dispersion problem

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