Hybrid approach for Pareto front expansion in heuristics

Yapicioglu H., Liu H., Smith A. E., Dozier G.

JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY, vol.62, no.2, pp.348-359, 2011 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 62 Issue: 2
  • Publication Date: 2011
  • Doi Number: 10.1057/jors.2010.151
  • Journal Name: JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Scopus
  • Page Numbers: pp.348-359
  • Keywords: kriging, general regression neural network, multi-objective optimization, heuristic search, hybrid methods, SIMULATION
  • Anadolu University Affiliated: Yes

Abstract

Heuristic search can be an effective multi-objective optimization tool; however, the required frequent function evaluations can exhaust computational sources. This paper explores using a hybrid approach with statistical interpolation methods to expand optimal solutions obtained by multiple criteria heuristic search. The goal is to significantly increase the number of Pareto optimal solutions while limiting computational effort. The interpolation approaches studied are kriging and general regression neural networks. This paper develops a hybrid methodology combining an interpolator with a heuristic, and examines performance on several non-linear bi-objective example problems. Computational experience shows this approach successfully expands and enriches the Pareto fronts of multi-objective optimization problems. Journal of the Operational Research Society (2011) 62, 348-359. doi:10.1057/jors.2010.151 Published online 27 October 2010