2023

Conférence

In proceedings of The 9th International Conference on Metaheuristics and Nature Inspired Computing META Marrakech, Nov 01-04, 2023

Hyperheuristics represent a generic method that provides a high level of abstraction, enabling solving several problems in the combinatorial optimization domain while reducing the need for human intervention in parameters tuning. This category consists in managing a set of low-level heuristics and attempting to find the optimal sequence that produces high-quality results. This paper proposes a hyperheuristic that simulates the honey bees mating behavior called “Honey bee Mating Optimization HyperHeuristic”  to solve the Patient Admission Scheduling Problem (PASP). The PASP is an NP-hard problem that represents an important field in the health care discipline. In order to perceive the influence of low-level heuristics on the model’s performance, we implemented two versions of the hyperheuristic that each one works on a different set of low-level heuristics. The results show that one of the versions generates better results than the other, revealing the important role of low-level heuristics’ quality leading to enhancing the hyperheuristic performance.

@InProceedings{10.1007/978-3-031-69257-4_7,
author="Oueslati, Imen
and Hammami, Moez
and Nouaouri, Issam
and Azzouz, Ameni
and Said, Lamjed Ben
and Allaoui, Hamid",
editor="Dorronsoro, Bernab{\'e}
and Ellaia, Rachid
and Talbi, El-Ghazali",
title="A Honey Bee Mating Optimization HyperHeuristic for Patient Admission Scheduling Problem",
booktitle="Metaheuristics and Nature Inspired Computing",
year="2024",
publisher="Springer Nature Switzerland",
address="Cham",
pages="89--104",
abstract="Hyperheuristics represent a generic method that provides a high level of abstraction, enabling solving several problems in the combinatorial optimization domain while reducing the need for human intervention in parameters tuning. This category consists in managing a set of low-level heuristics and attempting to find the optimal sequence that produces high-quality results. This paper proposes a hyperheuristic that simulates the honey bees mating behavior called ``Honey bee Mating Optimization HyperHeuristic'' ({\$}{\$}HBMOH^{\{}2{\}}{\$}{\$}HBMOH2) to solve the Patient Admission Scheduling Problem (PASP). The PASP is an NP-hard problem that represents an important field in the health care discipline. In order to perceive the influence of low-level heuristics on the model's performance, we implemented two versions of the hyperheuristic that each one works on a different set of low-level heuristics. The results show that one of the versions generates better results than the other, revealing the important role of low-level heuristics' quality leading to enhancing the hyperheuristic performance.",
isbn="978-3-031-69257-4"
}