A black-box scatter search for optimization problems with integer variables

Authors:
Manuel Laguna;Francisco Gortázar;Micael Gallego;Abraham Duarte;Rafael Martí
Affiliations:
Leeds School of Business, University of Colorado at Boulder, Boulder, USA;Departamento de Ciencias de la Computación, Universidad Rey Juan Carlos, Madrid, Spain;Departamento de Ciencias de la Computación, Universidad Rey Juan Carlos, Madrid, Spain;Departamento de Ciencias de la Computación, Universidad Rey Juan Carlos, Madrid, Spain;Departamento de Estadística e Investigación Operativa, Universitat de València, Valencia, Spain and Visiting the Leeds School of Business, University of Colorado at Boulder, Boulder ...
Venue:
Journal of Global Optimization
Year:
2014

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Abstract

The goal of this work is the development of a black-box solver based on the scatter search methodology. In particular, we seek a solver capable of obtaining high quality outcomes to optimization problems for which solutions are represented as a vector of integer values. We refer to these problems as integer optimization problems. We assume that the decision variables are bounded and that there may be constraints that require that the black-box evaluator is called in order to know whether they are satisfied. Problems of this type are common in operational research areas of applications such as telecommunications, project management, engineering design and the like.Our experimental testing includes 171 instances within four classes of problems taken from the literature. The experiments compare the performance of the proposed method with both the best context-specific procedures designed for each class of problem as well as context-independent commercial software. The experiments show that the proposed solution method competes well against commercial software and that can be competitive with specialized procedures in some problem classes.