DC-GRASP: directing the search on continuous-GRASP

Tiago Maritan Ugulino Araújo (), Lisieux Marie M. S. Andrade (), Carlos Magno (), Lucídio Anjos Formiga Cabral (), Roberto Quirino Nascimento () and Cláudio N. Meneses ()
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Tiago Maritan Ugulino Araújo: Universidade Federal da Paraíba
Lisieux Marie M. S. Andrade: Universidade Federal da Paraíba
Carlos Magno: Universidade Federal da Paraíba
Lucídio Anjos Formiga Cabral: Universidade Federal da Paraíba
Roberto Quirino Nascimento: Universidade Federal da Paraíba
Cláudio N. Meneses: Universidade Federal do ABC

Journal of Heuristics, 2016, vol. 22, issue 4, No 2, 365-382

Abstract: Abstract Several papers in the scientific literature use metaheuristics to solve continuous global optimization. To perform this task, some metaheuristics originally proposed for solving combinatorial optimization problems, such as Greedy Randomized Adaptive Search Procedure (GRASP), Tabu Search and Simulated Annealing, among others, have been adapted to solve continuous global optimization problems. Proposed by Hirsch et al., the Continuous-GRASP (C-GRASP) is one example of this group of metaheuristics. The C-GRASP is an adaptation of GRASP proposed to solve continuous global optimization problems under box constraints. It is simple to implement, derivative-free and widely applicable method. However, according to Hedar, due to its random construction, C-GRASP may fail to detect promising search directions especially in the vicinity of minima, which may result in a slow convergence. To minimize this problem, in this paper we propose a set of methods to direct the search on C-GRASP, called Directed Continuous-GRASP (DC-GRASP). The proposal is to combine the ability of C-GRASP to diversify the search over the space with some efficient local search strategies to accelerate its convergence. We compare the DC-GRASP with the C-GRASP and other metaheuristics from literature on a set of standard test problems whose global minima are known. Computational results show the effectiveness and efficiency of the proposed methods, as well as their ability to accelerate the convergence of the C-GRASP.

Keywords: C-GRASP; Metaheuristics; Continuous global optimization problems; Nonlinear programming (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10732-014-9278-6

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