Adaptive memory programming for constrained global optimization

Authors:
Leon Lasdon;Abraham Duarte;Fred Glover;Manuel Laguna;Rafael Martí
Affiliations:
Information, Risk, and Operations Management Department, The University of Texas at Austin, USA;Departamento de Ciencias de la Computación, Universidad Rey Juan Carlos, Spain;OptTek Systems, Inc., Boulder, CO 80302, USA;Leeds School of Business, University of Colorado at Boulder, USA;Departamento de Estadística e Investigación Operativa, Universidad de Valencia, Spain
Venue:
Computers and Operations Research
Year:
2010

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Abstract

The problem of finding a global optimum of a constrained multimodal function has been the subject of intensive study in recent years. Several effective global optimization algorithms for constrained problems have been developed; among them, the multi-start procedures discussed in Ugray et al. [1] are the most effective. We present some new multi-start methods based on the framework of adaptive memory programming (AMP), which involve memory structures that are superimposed on a local optimizer. Computational comparisons involving widely used gradient-based local solvers, such as Conopt and OQNLP, are performed on a testbed of 41 problems that have been used to calibrate the performance of such methods. Our tests indicate that the new AMP procedures are competitive with the best performing existing ones.