New variable neighbourhood search based 0-1 MIP heuristics

Hanafi, Saïd; Lazić, Jasmina; Mladenović, Nenad; Wilbaut, Christophe; Crévits, Igor

doi:10.2298/yjor140219014h

Cited by 23 publications

(9 citation statements)

References 16 publications

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“…() and Hanafi et al. () for variable neighborhood search. A generic algorithm is also provided in Rothberg () to “polish” primal solutions of an MIP in a postprocessing phase, providing quick improvement of already good solutions.…”

Section: Related Workmentioning

confidence: 99%

Parallel matheuristics for the discrete unit commitment problem with min‐stop ramping constraints

Dupin

Talbi

2018

Int Trans Operational Res

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The discrete unit commitment problem with min‐stop ramping constraints optimizes the daily production of thermal power plants, subject to an operational reactivity of thermal units in a 30‐minute delay. Previously, mixed integer programming (MIP) formulations aimed at an exact optimization approach. This paper derives matheuristics to face the short time limit imposed by the operational constraints. Continuous relaxations guide the search for feasible solutions exploiting tailored variable fixing strategies. Parallel matheuristics are derived considering complementary strategies in parallel. Tests were performed on more than 600 real‐life instances. Our parallel matheuristic provides high‐quality solutions and outperforms the MIP approach in the time limits imposed by the industrial application. This paper illustrates a special interest for matheuristics in industrial highly constrained problems: many tailored neighborhood searches can be derived from an MIP formulation, and their combination in a parallel scheme improves the solution quality as well as the consistency of the heuristic.

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Section: Related Workmentioning

confidence: 99%

Parallel matheuristics for the discrete unit commitment problem with min‐stop ramping constraints

Dupin

Talbi

2018

Int Trans Operational Res

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“…One jumps from the current solution to a new one if and only if a better solution has been found. Despite its simplicity, it proves to be effective for solving a set of combinatorial and global optimization problems (for recent surveys, see Hanafi, ; Hansen et al., ).…”

Section: Variable Neighborhood Searchmentioning

confidence: 99%

Exponential quality function for community detection in complex networks

Džamić

Pei

Marić

et al. 2018

Int Trans Operational Res

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One of the most popular topics in analyzing complex networks is the detection of its community structure. In this paper, we introduce a new criterion for community detection, called the E‐quality function. The quality of an individual community is defined as a difference between its benefit and its cost, where both are exponential functions of the number of internal edges and the number of external edges, respectively. The obtained optimization problem, maximization of the E‐quality function over all possible partitions of a network, is solved by the variable neighborhood search (VNS)‐based heuristic. Comparison of the new criterion and modularity is performed on the usual test instances from the literature. Experimental results obtained both on artificial and real networks show that the proposed E‐quality function allows detection of the communities existing in the network.

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“…Moreover, as it can be seen from [10], ILP formulation can be tackled by efficient metaheuristic approaches for obtaining suboptimal solutions for large dimensions.…”

Section: A Modified Integer Linear Programming Formulationmentioning

confidence: 99%