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dc.contributor.authorElgamal, Zenab Mohamed
dc.contributor.authorYasin, Norizan Binti Mohd
dc.contributor.authorTubishat, Mohammad
dc.contributor.authorAlswaitti, Mohammed
dc.contributor.authorMirjalili, Seyedali
dc.date.accessioned2020-10-27T04:53:39Z
dc.date.available2020-10-27T04:53:39Z
dc.date.issued2020
dc.identifier.issn2169-3536
dc.identifier.doi10.1109/access.2020.3029728
dc.identifier.urihttp://hdl.handle.net/10072/398769
dc.description.abstractHarris Hawks Optimization (HHO) algorithm is a new metaheuristic algorithm, inspired by the cooperative behavior and chasing style of Harris’ Hawks in nature called surprise pounce. HHO demonstrated promising results compared to other optimization methods. However, HHO suffers from local optima and population diversity drawbacks. To overcome these limitations and adapt it to solve feature selection problems, a novel metaheuristic optimizer, namely Chaotic Harris Hawks Optimization (CHHO), is proposed. Two main improvements are suggested to the standard HHO algorithm. The first improvement is to apply the chaotic maps at the initialization phase of HHO to enhance the population diversity in the search space. The second improvement is to use the Simulated Annealing (SA) algorithm to the current best solution to improve HHO exploitation. To validate the performance of the proposed algorithm, CHHO was applied on 14 medical benchmark datasets from the UCI machine learning repository. The proposed CHHO was compared with the original HHO and some famous and recent metaheuristics algorithms, containing Grasshopper Optimization Algorithm (GOA), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Butterfly Optimization Algorithm (BOA), and Ant Lion Optimizer (ALO). The used evaluation metrics include the number of selected features, classification accuracy, fitness values, Wilcoxon’s statistical test (
dc.description.peerreviewedYes
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartofpagefrom186638
dc.relation.ispartofpageto186652
dc.relation.ispartofjournalIEEE Access
dc.relation.ispartofvolume8
dc.subject.fieldofresearchInformation and Computing Sciences
dc.subject.fieldofresearchEngineering
dc.subject.fieldofresearchTechnology
dc.subject.fieldofresearchcode08
dc.subject.fieldofresearchcode09
dc.subject.fieldofresearchcode10
dc.titleAn Improved Harris Hawks Optimization Algorithm With Simulated Annealing for Feature Selection in the Medical Field
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationElgamal, ZM; Yasin, NBM; Tubishat, M; Alswaitti, M; Mirjalili, S, An Improved Harris Hawks Optimization Algorithm With Simulated Annealing for Feature Selection in the Medical Field, IEEE Access, 2020, 8, pp. 186638-186652
dcterms.licensehttp://creativecommons.org/licenses/by/4.0/
dc.date.updated2020-10-27T03:50:52Z
dc.description.versionVersion of Record (VoR)
gro.rights.copyright© The Author(s) 2020. This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
gro.hasfulltextFull Text
gro.griffith.authorMirjalili, Seyedali


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