Abstract
The venture of meta-heuristic optimization algorithms into the field of biology has only accelerated growth in swarm intelligence and evolutionary algorithms. We present one such novel approach, inspired by several avian algorithms, mimicking the lifestyle and breeding pattern of the Laysan Albatross. The proposed algorithm is suitable for any nonlinear continuous function optimization task and proves to perform better for training neural networks, compared to gradient-based approaches such as back-propagation and other bio-inspired approaches such as the Whale Optimization Algorithm. We also show that it is also not as susceptible to overfitting. We tested our algorithm on a total of 12 datasets and obtained an average train accuracy of 0.785, average training standard deviation of 0.037, average test accuracy of 0.793 and an average test standard deviation of 0.035.
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Krishnan, K., Subramaniasivam, A., Ravichandran, K., Subramanyam, N. (2022). Albatross Optimization Algorithm: A Novel Nature Inspired Search Algorithm. In: Noor, A., Sen, A., Trivedi, G. (eds) Proceedings of Emerging Trends and Technologies on Intelligent Systems . ETTIS 2021. Advances in Intelligent Systems and Computing, vol 1371. Springer, Singapore. https://doi.org/10.1007/978-981-16-3097-2_17
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DOI: https://doi.org/10.1007/978-981-16-3097-2_17
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