Abstract
In this paper, a novel Deep Self-Learning Approach applied to Artificial Orca Algorithm and based on mutation operators is proposed. The idea of contribution comes back to the stagnation that Swarm Intelligence Algorithms are facing. The proposed framework is based on two mutation operators known as Cauchy and Gaussian operators. To evaluate the proposed approach, it was addressed to a problem that affects the worldwide emergency teams, especially with the Covid-19 pandemic that the world is currently facing. This problem is known as Ambulance Dispatching and Emergency Calls Covering Problem, and was applied to real data of Saudi Arabia in Covid-19 Context. The results show that the Deep Self-Learning approach based on the Cauchy mutation operator well-manages the dispatching of emergency vehicles while respecting the cover of calls during a crisis period in the studied area.
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Acknowledgements
We would like to express our special thanks of gratitude to Prince Mohammad Bin Fahd Center for Futuristic Studies for the support of this work.
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Bendimerad, L.S., Drias, H. (2022). An Efficient Deep Self-learning Artificial Orca Algorithm for Solving Ambulance Dispatching and Calls Covering Problem. In: Abraham, A., et al. Proceedings of the 13th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2021). SoCPaR 2021. Lecture Notes in Networks and Systems, vol 417. Springer, Cham. https://doi.org/10.1007/978-3-030-96302-6_12
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