Abstract
Biogeography can be broken down into “bio” and “Geography” which would imply the geography, i.e., the dispersion of biological organisms. The entire field of biology-inspired algorithm is inclined toward providing the most optimal solution for a given problem set. Computer science experts want to always learn from the surroundings. Nature is sporadic and spontaneous and the erratic nature of a habitat is the very differentiating factor between a real world and an ideal world problem. Things change and that nothing remains constant. The diversification of a certain habitat is bound to change through external influences, some for the better, some for the worse. This paper tries to mimic the natural influences in a habitat in a Python environment and try to come up with a minimal objective value after iterating through the given meta-heuristic algorithm.
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Sharma, S., Chandrasekhar Rao, D. (2022). Biogeography-Based Optimization. In: Satapathy, S.C., Peer, P., Tang, J., Bhateja, V., Ghosh, A. (eds) Intelligent Data Engineering and Analytics. Smart Innovation, Systems and Technologies, vol 266. Springer, Singapore. https://doi.org/10.1007/978-981-16-6624-7_47
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DOI: https://doi.org/10.1007/978-981-16-6624-7_47
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