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dc.contributor.authorAbdollahzadeh, B
dc.contributor.authorGharehchopogh, FS
dc.contributor.authorMirjalili, S
dc.description.abstractMetaheuristics play a crucial role in solving optimization problems. The majority of such algorithms are inspired by collective intelligence and foraging of creatures in nature. In this paper, a new metaheuristic is proposed inspired by African vultures' lifestyle. The algorithm is named African Vultures Optimization Algorithm (AVOA) and simulates African vultures' foraging and navigation behaviors. To evaluate the performance of AVOA, it is first tested on 36 standard benchmark functions. A comparative study is then conducted that demonstrates the superiority of the proposed algorithm compared to several existing algorithms. To showcase the applicability of AVOA and its black box nature, it is employed to find optimal solutions for eleven engineering design problems. As per the experimental results, AVOA is the best algorithm on 30 out of 36 benchmark functions and provides superior performance on the majority of engineering case studies. Wilcoxon rank-sum test is used for statistical evaluation and indicates the significant superiority of the AVOA algorithm at a 95% confidence interval.
dc.publisherElsevier BV
dc.relation.ispartofjournalComputers and Industrial Engineering
dc.subject.fieldofresearchMathematical sciences
dc.titleAfrican vultures optimization algorithm: A new nature-inspired metaheuristic algorithm for global optimization problems
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationAbdollahzadeh, B; Gharehchopogh, FS; Mirjalili, S, African vultures optimization algorithm: A new nature-inspired metaheuristic algorithm for global optimization problems, Computers and Industrial Engineering, 2021, 158, pp. 107408
gro.hasfulltextNo Full Text
gro.griffith.authorMirjalili, Seyedali

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