TY - JOUR
T1 - African vultures optimization algorithm
T2 - A new nature-inspired metaheuristic algorithm for global optimization problems
AU - Abdollahzadeh, Benyamin
AU - Gharehchopogh, Farhad Soleimanian
AU - Mirjalili, Seyedali
N1 - Publisher Copyright:
© 2021
PY - 2021/8
Y1 - 2021/8
N2 - Metaheuristics 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.
AB - Metaheuristics 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.
KW - African vultures
KW - Algorithm
KW - Artificial Intelligence
KW - Artificial vulture optimization algorithm
KW - Benchmark
KW - Metaheuristic
KW - Optimization
KW - Soft Computing
UR - http://www.scopus.com/inward/record.url?scp=85108684499&partnerID=8YFLogxK
U2 - 10.1016/j.cie.2021.107408
DO - 10.1016/j.cie.2021.107408
M3 - Article
AN - SCOPUS:85108684499
SN - 0360-8352
VL - 158
JO - Computers and Industrial Engineering
JF - Computers and Industrial Engineering
M1 - 107408
ER -