A tree-seed algorithm based on intelligent search mechanisms for continuous optimization

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dc.contributor.authorKiran, Mustafa Servet
dc.contributor.authorHakli, Huseyin
dc.date.accessioned2024-02-23T14:02:10Z
dc.date.available2024-02-23T14:02:10Z
dc.date.issued2021
dc.departmentNEÜen_US
dc.description.abstractOne of the recently proposed metaheuristic algorithms is tree-seed algorithm, TSA for short. TSA is developed by inspiring the relation between trees and their seeds in order to solve continuous optimization problems, and it has a simple but effective algorithmic structure. The algorithm uses two different solution generating mechanisms in order to improve balance local and global search abilities. However, when the algorithm is analyzed in detail, it is seen that there are some issues in the basic algorithm. These are (i) when trees in the stand approaches to each other, the diversification in the stand is lost, (ii) there is no mechanism to get rid of local minima for a tree, (iii) some of the fitness calculation goes to waste due to seed generation mechanism of basic TSA. In order to address these issues, four different approaches (withering process, sequential seed generation, best-based solution update rule and dimensional selection for the solution update rule) have been proposed for the basic TSA, and all these approaches have been also integrated within algorithmic framework of TSA, named new tree-seed algorithm briefly NTSA, and each of them has been used to solve 28 CEC2013 benchmark functions. In the experimental comparisons, the variants of TSA have been compared with each other, and the better algorithm, NTSA, has been compared with 17 state-of-art algorithms such as artificial bee colony, particle swarm optimization, differential evolution, genetic algorithm, covariance matrix adaptation evolutionary strategy etc. The experimental analysis and comparisons show that the NTSA shows better or similar performance than/with the compared algorithms in terms of solution quality and robustness. (C) 2020 Elsevier B.V. All rights reserved.en_US
dc.identifier.doi10.1016/j.asoc.2020.106938
dc.identifier.issn1568-4946
dc.identifier.issn1872-9681
dc.identifier.scopus2-s2.0-85097095823en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.asoc.2020.106938
dc.identifier.urihttps://hdl.handle.net/20.500.12452/11619
dc.identifier.volume98en_US
dc.identifier.wosWOS:000603366000006en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofApplied Soft Computingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectTree-Seed Algorithmen_US
dc.subjectWithering Processen_US
dc.subjectSequential Seed Generationen_US
dc.subjectBest-Based Update Ruleen_US
dc.subjectDimensional Selectionen_US
dc.titleA tree-seed algorithm based on intelligent search mechanisms for continuous optimizationen_US
dc.typeArticleen_US

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