Abstract
This chapter introduces Multi-Objective Artificial Hummingbird Algorithm (MOAHA), a multi-objective variation of the newly established Artificial Hummingbird Algorithm (AHA). The AHA algorithm simulates the specific flight skills and intelligent search strategies of hummingbirds in the wild. Three types of flight skills are used in food search strategies, including axial, oblique, and all-round flights. Multi-objective AHA is tested through 5 real-world engineering case studies. Various performance indicators, such as Spacing (S), Inverted Generational Distance (IGD), and Maximum Spread (MS), are used to compare the MOAHA to the MOPSO, MOWOA, and MOHHO. The suggested algorithm may produce quality Pareto fronts with appropriate precision, uniformity, and very competitive outcomes, according to the qualitative and quantitative.
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Khodadadi, N., Mirjalili, S.M., Zhao, W., Zhang, Z., Wang, L., Mirjalili, S. (2023). Multi-Objective Artificial Hummingbird Algorithm. In: Biswas, A., Kalayci, C.B., Mirjalili, S. (eds) Advances in Swarm Intelligence. Studies in Computational Intelligence, vol 1054. Springer, Cham. https://doi.org/10.1007/978-3-031-09835-2_22
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