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Improving performance of a hyper-heuristic using a multilayer perceptron for vehicle routing

Tyasnurita, Raras; Özcan, Ender; Shahriar, Asta; John, Robert

Authors

Raras Tyasnurita rxt@cs.nott.ac.uk

Asta Shahriar sba@cs.nott.ac.uk

Robert John rij@cs.nott.ac.uk



Abstract

A hyper-heuristic is a heuristic optimisation method which generates or selects heuristics (move operators) based on a set of components while solving a computationally difficult problem. Apprenticeship learning arises while observing the behavior of an expert in action. In this study, we use a multilayer perceptron (MLP) as an apprenticeship learning algorithm to improve upon the performance of a state-of-the-art selection hyper-heuristic used as an expert, which was the winner of a cross-domain heuristic search challenge (CHeSC 2011). We collect data based on the relevant actions of the expert while solving selected vehicle routing problem instances from CHeSC 2011. Then an MLP is trained using this data to build a selection hyper-heuristic consisting of a number classifiers for heuristic selection, parameter control, and move-acceptance. The generated selection hyper-heuristic is tested on the unseen vehicle routing problem instances. The empirical results indicate the success of MLP-based hyper-heuristic achieving a better performance than the expert and some previously proposed algorithms.

Publication Date Sep 7, 2015
Peer Reviewed Peer Reviewed
APA6 Citation Tyasnurita, R., Özcan, E., Shahriar, A., & John, R. (2015). Improving performance of a hyper-heuristic using a multilayer perceptron for vehicle routing
Keywords Multilayer Perceptron, Hyper-heuristic, Vehicle Routing,
Apprenticeship Learning
Related Public URLs http://www.ukci2015.ex.ac.uk
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingh.../end_user_agreement.pdf

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Copyright Statement
Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf





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