Path relinking for large-scale global optimization
- 255 Downloads
In this paper we consider the problem of finding a global optimum of a multimodal function applying path relinking. In particular, we target unconstrained large-scale problems and compare two variants of this methodology: the static and the evolutionary path relinking (EvoPR). Both are based on the strategy of creating trajectories of moves passing through high-quality solutions in order to incorporate their attributes to the explored solutions. Computational comparisons are performed on a test-bed of 19 global optimization functions previously reported with dimensions ranging from 50 to 1,000, totalizing 95 instances. Our results show that the EvoPR procedure is competitive with the state-of-the-art methods in terms of the average optimality gap achieved. Statistical analysis is applied to draw significant conclusions.
KeywordsEvolutionary algorithms Path relinking Metaheuristics Global optimization
This research has been partially supported by the Ministerio de Educación y Ciencia of Spain (TIN2009-07516). We would like to thank Profs. Glover and Resende for their descriptions and suggestions on the Path Relinking and Evolutionary Path Relinking methodologies, respectively.
- Andrade DV, Resende MGC (2007) GRASP with evolutionary path-relinking. In: Proceedings of seventh metaheuristics international conference (MIC 2007)Google Scholar
- Auger A, Hansen N (2005) A restart CMA evolution strategy with increasing population size. In: Proceedings of 2005 IEEE congress on evolutionary computation (CEC’2005), pp 1769–1776Google Scholar
- Duarte A, Martí R, Glover F, Gortazar F (2010) Hybrid scatter tabu search for unconstrained global optimization. Ann Oper Res. doi: 10.1007/s10479-009-0596-2
- Eshelman LJ, Schaffer JD (1993) Real-coded genetic algorithm and interval schemata. In: Foundation of genetic algorithms, pp 187–202Google Scholar
- Hansen N (2006) Compilation of results on the 2005 CEC benchmark function set. Technical report, CoLAB Institute of Computational Sciences ETH, ZurichGoogle Scholar
- Herrera F, Lozano M (2009) Workshop on evolutionary algorithms and other metaheuristics for continuous optimization problems—a scalability test. http://sci2s.ugr.es/programacion/workshop/Scalability.html
- Herrera F, Lozano M, Molina D (2010a) Test suite for the special issue of soft computing on scalability of evolutionary algorithms and other metaheuristics for large scale continuous optimization problems. Technical report, University of GranadaGoogle Scholar
- Herrera F, Lozano M, Molina D (2010b) Components and parameters of DE. Real-coded CHC and G-CMAES. Technical report, University of GranadaGoogle Scholar
- Hvattum LM, Duarte A, Martí R, Glover F (2010) The improvement method in scatter search: an experimental study on global optimization. Technical report, University of ValenciaGoogle Scholar
- Resende MGC, Ribeiro CC (2003) Greedy randomized adaptive search procedures. In: Glover F, Kochenberger G (eds) Handbook of metaheuristics. Kluwer, Dordrecht, pp 219–250Google Scholar
- Suganthan PN, Hansen N, Liang JJ, Deb K, Chen Y-P, Auger A, Tiwari S (2005) Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization. Technical report, Nanyang Technological University of SingaporeGoogle Scholar