Solving Graph Partitioning Problems with Parallel Metaheuristics

Kokosiński, Zbigniew; Bała, Marcin

doi:10.1007/978-3-319-59861-1_6

Cited by 4 publications

(1 citation statement)

References 11 publications

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“…During the last decade, various local search heuristics and metaheuristics have been proposed for the MSCP. These include the Tabu Search algorithm of Bouziri and Jouini [20], the Multi-Neighbourhood search of Helmar and Chiarandini [21], the Breakout Local Search algorithm of Benlic and Hao [22], the Memetic Algorithms of Moukrim et al [23] and Jin et al [24], the Hybrid Evolutionary Search of Jin and Hao [25], the D-Wave Quantum Computer-based approach of Mahasinghe et al [26], the Parallel Metaheuristics of Kokosiński and Bała [27], and the bi-objective genetic algorithm of Harrabi and Siala [28].…”

Section: Introductionmentioning

confidence: 99%

A Column Generation-Based Lower Bound for the Minimum Sum Coloring Problem

et al. 2020

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The objective of this paper is to derive a tight and efficient lower bound for the minimum sum coloring problem. This NP-hard problem is a variant of the classical graph coloring problem where the objective is to minimize the sum of the colors. A column generation approach is proposed to solve the linear relaxation of a set partition-based formulation. Various enhancements are proposed in order to efficiently obtain attractive columns while avoiding as much as possible the exact solution of the huge number of the NP-hard pricing problems. Experimental results conducted on 42 hard benchmark instances show an average reduction of 89.73% of the gap between the best known lower and upper bounds, including 14 new optimality results.

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