Speeding up continuous GRASP

• Published in: European journal of operational research Vol. 205; no. 3; pp. 507 - 521
• Main Authors: Hirsch, M.J., Pardalos, P.M., Resende, M.G.C.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 16.09.2010; Elsevier; Elsevier Sequoia S.A
• Series: European Journal of Operational Research
• Subjects: Algorithms; Applied sciences; Benchmarking; Comparative analysis; Construction costs; Continuous GRASP; Continuous optimization; Exact sciences and technology; Global optimization; GRASP; GRASP Continuous GRASP Global optimization Multimodal functions Continuous optimization Heuristic Stochastic algorithm Stochastic local search Nonlinear programming; Heuristic; Minima; Multimodal functions; Nonlinear programming; Operational research; Operational research and scientific management; Operational research. Management science; Optimization; Optimization algorithms; Searching; Stochastic algorithm; Stochastic local search; Stochastic models; Stochasticity; Studies; Global optimization; Stochastic local search; Continuous optimization; Continuous GRASP; Stochastic algorithm; Nonlinear programming; GRASP; Multimodal functions; Heuristic; Gripping; Local search; Costs; Global optimum; Non linear programming; Randomized algorithm; Global solution; Optimization; Stochastic programming; Stopping rule; Efficiency; Heuristic method; Greedy algorithm; Metamodel
• ISSN: 0377-2217; 1872-6860
• DOI: 10.1016/j.ejor.2010.02.009

Continuous GRASP (C-GRASP) is a stochastic local search metaheuristic for finding cost-efficient solutions to continuous global optimization problems subject to box constraints (Hirsch et al., 2007). Like a greedy randomized adaptive search procedure (GRASP), a C-GRASP is a multi-start procedure where a starting solution for local improvement is constructed in a greedy randomized fashion. In this paper, we describe several improvements that speed up the original C-GRASP and make it more robust. We compare the new C-GRASP with the original version as well as with other algorithms from the recent literature on a set of benchmark multimodal test functions whose global minima are known. Hart’s sequential stopping rule (1998) is implemented and C-GRASP is shown to converge on all test problems.