A Meta-learning Approach to Recommend the Meta-heuristic Algorithm Based on Instance Features

DOI

10.2991/978-94-6463-056-5_23How to use a DOI?

Keywords

algorithm recommendation; meta-learning; swarm intelligence optimization algorithms; vehicle routing problem

Abstract

This paper studies a framework for implementing meta-heuristic algorithm selection based on meta-learning approach, which is used to recommend the most suitable meta-heuristic algorithm for different problem instances instantly. Therefore, a small sample of instances for capacitated vehicle routing problem (CVRP) is selected as an experimental data set, artificial bee colony, particle swarm optimization, ant colony, artificial fish colony and genetic algorithm which are selected as the recommended algorithms. This study establishes the classification label corresponding to the problem and the algorithm by running the optimization algorithms. The meta-knowledge base corresponding to the feature and the label is generated by extracting a set of instance features. When a new instance is given, its features only need to be extracted to recommend an algorithm. In the process, three meta-learning algorithms of random forest, BP neural network and K-nearest neighbor are used to train meta-model, and comparative analysis is applied. The experimental results show that the average recommendation accuracy is about 80%. The algorithm recommendation framework based on instance features can be extended to similar applications.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Jianshuang Cui
AU  - Fan Yang
PY  - 2022
DA  - 2022/12/29
TI  - A Meta-learning Approach to Recommend the Meta-heuristic Algorithm Based on Instance Features
BT  - Proceedings of the 2022 2nd International Conference on Management Science and Software Engineering (ICMSSE 2022)
PB  - Atlantis Press
SP  - 161
EP  - 167
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-056-5_23
DO  - 10.2991/978-94-6463-056-5_23
ID  - Cui2022
ER  -