African vultures optimization algorithm: A new nature-inspired metaheuristic algorithm for global optimization problems

Benyamın Abdollahzadeh

doi:10.1016/j.cie.2021.107408

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African vultures optimization algorithm: A new nature-inspired metaheuristic algorithm for global optimization problems

2021-08-01

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Benyamın Abdollahzadeh

Farhad Soleimanian Gharehchopogh *

Seyedali Mirjalili

DOI：10.1016/j.cie.2021.107408

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Abstract

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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.

Keywords:

Metaheuristic

Algorithm

Artificial vulture optimization algorithm

African vultures

Optimization

Artificial Intelligence

Benchmark

Soft Computing

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