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|a 10.1007/s11227-022-04869-7
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|a eng
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|a Chen, Lei
|e verfasserin
|4 aut
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|a Harris hawks optimization based on global cross-variation and tent mapping
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|c 2023
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 24.02.2023
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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|a Harris hawks optimization (HHO) is a new meta-heuristic algorithm that builds a model by imitating the predation process of Harris hawks. In order to solve the problems of poor convergence speed caused by uniform choice position update formula in the exploration stage of basic HHO and falling into local optimization caused by insufficient population richness in the later stage of the algorithm, a Harris hawks optimization based on global cross-variation and tent mapping (CRTHHO) is proposed in this paper. Firstly, the tent mapping is introduced in the exploration stage to optimize random parameter q to speed up the convergence in the early stage. Secondly, the crossover mutation operator is introduced to cross and mutate the global optimal position in each iteration process. The greedy strategy is used to select, which prevents the algorithm from falling into local optimal because of skipping the optimal solution and improves the convergence accuracy of the algorithm. In order to investigate the performance of CRTHHO, experiments are carried out on ten benchmark functions and the CEC2017 test set. Experimental results show that the CRTHHO algorithm performs better than the HHO algorithm and is competitive with five advanced meta-heuristic algorithms
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|a Journal Article
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|a Crossover mutation
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|a Greedy selection
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|a Harris hawks optimization
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|a Meta-heuristic algorithm
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| 650 |
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|a Tent mapping
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| 700 |
1 |
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|a Song, Na
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Ma, Yunpeng
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t The Journal of supercomputing
|d 1998
|g 79(2023), 5 vom: 18., Seite 5576-5614
|w (DE-627)NLM098252410
|x 0920-8542
|7 nnas
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| 773 |
1 |
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|g volume:79
|g year:2023
|g number:5
|g day:18
|g pages:5576-5614
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|u http://dx.doi.org/10.1007/s11227-022-04869-7
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