Chaotic Harris Hawk Optimization Algorithm for Training Feed-Forward Neural Network

Atta, Eman A; Ali, Ahmed Fouad; Elshamy, Ahmed A

doi:10.1007/978-3-030-89701-7_33

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2022

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“…They used two popular classification datasets to test their proposed approach. Moreover, Atta et al [239] applied their enhanced version of the HHO in order to train a feed-forward neural network. They compared their method with eight metaheurstic algorithms using five classification datasets.…”

Section: Unit Commitment Problemmentioning

confidence: 99%

Recent Advances in Harris Hawks Optimization: A Comparative Study and Applications

et al. 2022

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The Harris hawk optimizer is a recent population-based metaheuristics algorithm that simulates the hunting behavior of hawks. This swarm-based optimizer performs the optimization procedure using a novel way of exploration and exploitation and the multiphases of search. In this review research, we focused on the applications and developments of the recent well-established robust optimizer Harris hawk optimizer (HHO) as one of the most popular swarm-based techniques of 2020. Moreover, several experiments were carried out to prove the powerfulness and effectivness of HHO compared with nine other state-of-art algorithms using Congress on Evolutionary Computation (CEC2005) and CEC2017. The literature review paper includes deep insight about possible future directions and possible ideas worth investigations regarding the new variants of the HHO algorithm and its widespread applications.

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Section: Unit Commitment Problemmentioning

confidence: 99%