Hybrid approach for Pareto front expansion in heuristics

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...

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Bibliographic Details
Published in:The Journal of the Operational Research Society 2011-02, Vol.62 (2), p.348-359
Main Authors: Yapicioglu, H, Liu, H, Smith, AE, Dozier, G
Format: Article
Language:English
Subjects:
Artificial neural networks
Business and Management
Covariance
Estimate reliability
Genetic algorithms
Heuristic
Heuristics
Kriging
Management
Mathematical functions
Mathematical programming
Mops
Neural networks
Objective functions
Operations research
Operations Research/Decision Theory
Optimization
Pareto efficiency
Pareto optimum
Special Issue Paper
Special Issue Papers
Studies
Variables
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Summary: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.
ISSN:0160-5682
1476-9360
DOI:10.1057/jors.2010.151