Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/139628
Full metadata record
DC FieldValueLanguage
dc.contributor.authorWu, Leien_US
dc.contributor.authorLiu, Qien_US
dc.contributor.authorTian, Xueen_US
dc.contributor.authorZhang, Jixuen_US
dc.contributor.authorXiao, Wenshengen_US
dc.date.accessioned2020-05-20T08:50:52Z-
dc.date.available2020-05-20T08:50:52Z-
dc.date.issued2017-
dc.identifier.citationWu, L., Liu, Q., Tian, X., Zhang, J., & Xiao, W. (2018). A new improved fruit fly optimization algorithm IAFOA and its application to solve engineering optimization problems. Knowledge-Based Systems, 144, 153-173. doi:10.1016/j.knosys.2017.12.031en_US
dc.identifier.issn0950-7051en_US
dc.identifier.urihttps://hdl.handle.net/10356/139628-
dc.description.abstractNature-inspired algorithms are widely used in mathematical and engineering optimization. As one of the latest swarm intelligence-based methods, fruit fly optimization algorithm (FOA) was proposed inspired by the foraging behavior of fruit fly. In order to overcome the shortcomings of original FOA, a new improved fruit fly optimization algorithm called IAFOA is presented in this paper. Compared with original FOA, IAFOA includes four extra mechanisms: 1) adaptive selection mechanism for the search direction, 2) adaptive adjustment mechanism for the iteration step value, 3) adaptive crossover and mutation mechanism, and 4) multi-sub-swarm mechanism. The adaptive selection mechanism for the search direction allows the individuals to search for global optimum based on the experience of the previous iteration generations. According to the adaptive adjustment mechanism, the iteration step value can change automatically based on the iteration number and the best smell concentrations of different generations. Besides, the adaptive crossover and mutation mechanism introduces crossover and mutation operations into IAFOA, and advises that the individuals with different fitness values should be operated with different crossover and mutation probabilities. The multi-sub-swarm mechanism can spread optimization information among the individuals of the two sub-swarms, and quicken the convergence speed. In order to take an insight into the proposed IAFOA, computational complexity analysis and convergence analysis are given. Experiment results based on a group of 29 benchmark functions show that IAFOA has the best performance among several intelligent algorithms, which include five variants of FOA and five advanced intelligent optimization algorithms. Then, IAFOA is used to solve three engineering optimization problems for the purpose of verifying its practicability, and experiment results show that IAFOA can generate the best solutions compared with other ten algorithms.en_US
dc.language.isoenen_US
dc.relation.ispartofKnowledge-Based Systemsen_US
dc.rights© 2017 Elsevier B.V. All rights reserved.en_US
dc.subjectEngineering::Civil engineeringen_US
dc.titleA new improved fruit fly optimization algorithm IAFOA and its application to solve engineering optimization problemsen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Civil and Environmental Engineeringen_US
dc.contributor.organizationMaritime Institute @NTUen_US
dc.identifier.doi10.1016/j.knosys.2017.12.031-
dc.identifier.scopus2-s2.0-85039965415-
dc.identifier.volume144en_US
dc.identifier.spage153en_US
dc.identifier.epage173en_US
dc.subject.keywordsFruit Fly Optimization Algorithmen_US
dc.subject.keywordsOptimal Search Directionen_US
item.fulltextNo Fulltext-
item.grantfulltextnone-
Appears in Collections:CEE Journal Articles

SCOPUSTM   
Citations 5

43
Updated on Mar 10, 2021

PublonsTM
Citations 10

31
Updated on Mar 7, 2021

Page view(s)

156
Updated on Jul 3, 2022

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.