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A New Hybrid Algorithm for Continuous Optimization Problem

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dc.contributor.author Farnad, Behnam
dc.contributor.author Jafarian, Ahmad
dc.contributor.author Baleanu, Dumitru
dc.date.accessioned 2020-03-31T20:01:10Z
dc.date.available 2020-03-31T20:01:10Z
dc.date.issued 2018-03
dc.identifier.citation Farnad, Behnam; Jafarian, Ahmad; Baleanu, Dumitru, "A new hybrid algorithm for continuous optimization problem", Applied Mathematical Modelling, Vol. 55, pp. 652-673, (2018) tr_TR
dc.identifier.issn 0307-904X
dc.identifier.uri http://hdl.handle.net/20.500.12416/2802
dc.description.abstract This paper applies a new hybrid method by a combination of three population base algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Symbiotic Organisms Search (SOS). The proposed method has been inspired from natural selection process and it completes this process in GA by using the PSO and SOS. It tends to minimize the execution time and in addition to reduce the complexity. Symbiotic organisms search is a robust and powerful metaheuristic algorithm which has attracted increasing attention in recent decades. There are three alternative phases in the proposed algorithm: GA, which develops and selects best population for the next phases, PSO, which gets experiences for each appropriate solution and updates them as well and SOS, which benefits from previous phases and performs symbiotic interaction update phases in the real-world population. The proposed algorithm was tested on the set of best known unimodal and multimodal benchmark functions in various dimensions. It has further been evaluated in, the experiment on the clustering of benchmark datasets. The obtained results from basic and non-parametric statistical tests confirmed that this hybrid method dominates in terms of convergence, execution time, success rate. It optimizes the high dimensional and complex functions Rosenbrock and Griewank up to 10(-330) accuracy in less than 3 s, outperforming other known algorithms. It had also applied clustering datasets with minimum intra-cluster distance and error rate. (C) 2017 Elsevier Inc. All rights reserved. tr_TR
dc.language.iso eng tr_TR
dc.publisher Elsevier Science INC tr_TR
dc.relation.isversionof 10.1016/j.apm.2017.10.001 tr_TR
dc.rights info:eu-repo/semantics/closedAccess tr_TR
dc.subject Genetic Algorithms tr_TR
dc.subject Particle Swarm Optimization tr_TR
dc.subject Symbiotic Organisms Search tr_TR
dc.subject Global Optimization tr_TR
dc.subject Hybrid Algorithm tr_TR
dc.subject Data Clustering tr_TR
dc.title A New Hybrid Algorithm for Continuous Optimization Problem tr_TR
dc.type article tr_TR
dc.relation.journal Applied Mathematical Modelling tr_TR
dc.contributor.authorID 56389 tr_TR
dc.identifier.volume 55 tr_TR
dc.identifier.startpage 652 tr_TR
dc.identifier.endpage 673 tr_TR
dc.contributor.department Çankaya Üniversitesi, Fen Edebiyat Fakültesi, Matematik Bölümü tr_TR


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