An improved elephant herding optimization using sine–cosine mechanism and opposition based learning for global optimization problems

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dc.authorid0000-0002-8929-3473
dc.authorid0000-0003-1840-9958
dc.contributor.authorMuthusamy, Hariharan
dc.contributor.authorRavindran, Sindhu
dc.contributor.authorYaacob, Sazali
dc.contributor.authorPolat, Kemal
dc.date.accessioned2021-06-23T18:57:00Z
dc.date.available2021-06-23T18:57:00Z
dc.date.issued2021
dc.departmentBAİBÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.description.abstractAn improved elephant herding optimization (EHOI) is proposed for continuous function optimization, financial stress prediction problem and two engineering optimization problems in this work. Elephant Herding Optimization (EHO) is a swarm-based algorithm and was inspired by the social behaviour of elephant clans. In the literature, EHO has received great attention from researchers due to its global optimization capability and ease of implementation. However, it has few limitations like random replacing of worst individual and lack of exploitation, which leads to slow convergence. In this work, EHO was enhanced with the help of the position updating mechanism of sine–cosine algorithm (SCA) and opposition-based learning (OBL). The separating operator in original EHO was replaced by the sine–cosine mechanism and followed by opposition-based learning was introduced to increase the performance of EHO. The proposed EHOI was compared with eight well-known meta-heuristic optimization algorithms (MAs) by using 23 classical benchmark functions, 10 modern CEC2019 benchmark test functions and two engineering optimization problems. From the results, it was observed that the proposed EHOI outperformed most of the selected MAs in terms of solution quality. A kernel extreme learning machine (KELM) model was optimized by improved EHO and applied to handle financial stress prediction. The efficiency of the proposed EHOI_KELM model was tested on two popular financial datasets and compared with popular classifiers, EHO_KELM and SCA_KELM models. The results demonstrate that the proposed EHOI_KELM model shows excellent performance than the popular classifiers, EHO_KELM & SCA_KELM models and it can also serve as an effective tool for financial prediction.en_US
dc.description.sponsorshipAuthors would like to gratefully acknowledge the anonymous reviewers for providing their constructive comments, which considerably improved the quality of the paper.en_US
dc.identifier.doi10.1016/j.eswa.2021.114607
dc.identifier.issn0957-4174
dc.identifier.scopus2-s2.0-85100561014en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.eswa.2021.114607
dc.identifier.urihttps://hdl.handle.net/20.500.12491/5114
dc.identifier.volume172en_US
dc.identifier.wosWOS:000633045900006en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorPolat, Kemal
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofExpert Systems with Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectElephant Herding Optimizationen_US
dc.subjectFinancial Stress Prediction and Global Optimizationen_US
dc.subjectOpposition-Based Learningen_US
dc.subjectSine-Cosine Mechanismen_US
dc.titleAn improved elephant herding optimization using sine–cosine mechanism and opposition based learning for global optimization problemsen_US
dc.typeArticleen_US

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