Abstract
The dynamic implementation of meta-heuristic and evolutionary algorithms has transformed computational intelligence’s panoramic view. Considering the applicability of Nature-Inspired Algorithms in the view of the COVID-19 pandemic, the authors implemented the Honeybee Mating Optimization (HBMO), and Ant Colony Optimization (ACO) for efficiently travelling from different cities in vulnerability zone areas. The pheromone matrix and cost matrix were formulated using the HBMO algorithm and fed the aftermaths to map into the Ant Colony Optimization algorithm. The higher COVID-19 regions are denoted with less pheromone level, and the paths with a lower risk of getting infected comprise higher pheromone levels and vice versa. The authors featured the travel guide mapping of several cities of India and calculated the travelling probabilities for ensuring risk-free journeys. The four different threshold criteria maintained for the travelling probabilities are extremely safe conditions, moderately safe conditions, just safe conditions, and not safe conditions.
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Saket, S., Mishra, S.P., Bhattacharjee, V., Mishra, K.N. (2023). Travelling Guidance Using ACO and HBMO Techniques in COVID-19 Pandemics: A Novel Approach. In: Mandal, J.K., De, D. (eds) Frontiers of ICT in Healthcare . Lecture Notes in Networks and Systems, vol 519. Springer, Singapore. https://doi.org/10.1007/978-981-19-5191-6_38
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DOI: https://doi.org/10.1007/978-981-19-5191-6_38
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