An enhanced sea-horse optimizer for solving global problems and cluster head selection in wireless sensor networks

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dc.authoridHoussein, Essam Halim/0000-0002-8127-7233en_US
dc.authorscopusid43361385400en_US
dc.authorscopusid57215044860en_US
dc.authorscopusid55354654200en_US
dc.authorscopusid7401490255en_US
dc.authorscopusid57907429700en_US
dc.authorscopusid25960700400en_US
dc.authorwosidHoussein, Essam Halim/C-8941-2016en_US
dc.contributor.authorHoussein, Essam H.
dc.contributor.authorSaad, Mohammed R.
dc.contributor.authorCelik, Emre
dc.contributor.authorHu, Gang
dc.contributor.authorAli, Abdelmgeid A.
dc.contributor.authorShaban, Hassan
dc.date.accessioned2024-08-23T16:07:10Z
dc.date.available2024-08-23T16:07:10Z
dc.date.issued2024en_US
dc.departmentDüzce Üniversitesien_US
dc.description.abstractAn efficient variant of the recent sea horse optimizer (SHO) called SHO-OBL is presented, which incorporates the opposition-based learning (OBL) approach into the predation behavior of SHO and uses the greedy selection (GS) technique at the end of each optimization cycle. This enhancement was created to avoid being trapped by local optima and to improve the quality and variety of solutions obtained. However, the SHO can occasionally be vulnerable to stagnation in local optima, which is a problem of concern given the low diversity of sea horses. In this paper, an SHO-OBL is suggested for the tackling of genuine and global optimization systems. To investigate the validity of the suggested SHO-OBL, it is compared with nine robust optimizers, including differential evolution (DE), grey wolf optimizer (GWO), moth-flame optimization algorithm (MFO), sine cosine algorithm (SCA), fitness dependent optimizer (FDO), Harris hawks optimization (HHO), chimp optimization algorithm (ChOA), Fox optimizer (FOX), and the basic SHO in ten unconstrained test routines belonging to the IEEE congress on evolutionary computation 2020 (CEC'20). Furthermore, three different design engineering issues, including the welded beam, the tension/compression spring, and the pressure vessel, are solved using the proposed SHO-OBL to test its applicability. In addition, one of the most successful approaches to data transmission in a wireless sensor network that uses little energy is clustering. In this paper, SHO-OBL is suggested to assist in the process of choosing the optimal power-aware cluster heads based on a predefined objective function that takes into account the residual power of the node, as well as the sum of the powers of surrounding nodes. Similarly, the performance of SHO-OBL is compared to that of its competitors. Thorough simulations demonstrate that the suggested SHO-OBL algorithm outperforms in terms of residual power, network lifespan, and extended stability duration.en_US
dc.description.sponsorshipMinia Universityen_US
dc.description.sponsorshipNo Statement Availableen_US
dc.identifier.doi10.1007/s10586-024-04368-9
dc.identifier.issn1386-7857
dc.identifier.issn1573-7543
dc.identifier.scopus2-s2.0-85189164777en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.1007/s10586-024-04368-9
dc.identifier.urihttps://hdl.handle.net/20.500.12684/14531
dc.identifier.wosWOS:001194860400001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofCluster Computing-The Journal of Networks Software Tools and Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectSea-horse optimizeren_US
dc.subjectOpposition-based learningen_US
dc.subjectGreedy selectionen_US
dc.subjectMetaheuristicen_US
dc.subjectClusteringen_US
dc.subjectDifferential Evolutionen_US
dc.subjectHarmony Searchen_US
dc.subjectAlgorithmen_US
dc.subjectHybriden_US
dc.titleAn enhanced sea-horse optimizer for solving global problems and cluster head selection in wireless sensor networksen_US
dc.typeArticleen_US

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