Barnacles Mating Optimizer: A new bio-inspired algorithm for solving engineering optimization problems
By Mohd Herwan Sulaiman, Zuriani Mustaffa, Mohd Mawardi Saari, Hamdan Daniyal. Engineering applications of artificial intelligence, 2020
359 citations recorded by the source corpus.
Abstract This paper presents a novel bio-inspired optimization algorithm namely the Barnacles Mating Optimizer (BMO) algorithm to solve optimization problems. The proposed algorithm mimics the mating behaviour of barnacles in nature for solving optimization problems. The BMO is first benchmarked on a set of 23 mathematical functions to test the characteristics of BMO in finding the optimal solutions. It is then applied to optimal reactive power dispatch (ORPD) problem to verify the reliability and efficiency of BMO. Extensive comparative studies with other algorithms are conducted and from the simulation results, it is observed that BMO generally provides better results and exhibits huge potential of BMO in solving real optimization problems.
Subfields: Artificial Intelligence, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/623bd5d28b826b063509047af250d081a2c63aa1
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Citations in the corpus
- Hippopotamus optimization algorithm: a novel nature-inspired optimization algorithm (2024, methodology): “The Barnacles Mating Optimizer (BMO) 82 algorithm takes inspiration from the mating behavior observed in barna-cles in their natural habitat.”