Emperor penguin optimizer
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Emperor penguin optimizer (EPO) is an optimization algorithm developed by Gaurav Dhiman and Vijay Kumar in 2018.[1] It was inspired by the huddles of emperor penguin species to survive during the Antarctic winter. The huddling behavior of emperor penguins is decomposed into four phases:
- Generate and determine the huddle boundary of emperor penguins.
- Calculate the temperature profile around the huddle.
- Determine the distance between emperor penguins.
- Relocate the effective mover.[2]
An essential feature of this huddling behaviour is that each penguin has an equal opportunity to the warmth of the huddle. The main steps of EPO are to generate the huddle boundary, compute temperature around the huddle, calculate the distance, and find the effective mover. [1]
References[edit]
- ↑ 1.0 1.1 Dhiman, Gaurav; Kumar, Vijay (2018-11-01). "Emperor penguin optimizer: A bio-inspired algorithm for engineering problems". Knowledge-Based Systems. 159: 20–50. doi:10.1016/j.knosys.2018.06.001. ISSN 0950-7051. Unknown parameter
|s2cid=ignored (help) - ↑ Waters, Aaron; Blanchette, François; Kim, Arnold D. (2012-11-16). "Modeling Huddling Penguins". PLOS ONE. 7 (11): e50277. Bibcode:2012PLoSO...750277W. doi:10.1371/journal.pone.0050277. ISSN 1932-6203. PMC 3500382. PMID 23166841.
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