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

Abstract

Abstract 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' lifestyles. 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 conducted that demonstrates the superiority of the proposed algorithm compared to several existing algorithms. To showcase the applicability of the AVOA and its black box nature, it is employed to find optimal solutions for eleven engineering design problems. Various experiments show that the AVOA performs better than the comparative algorithms in most engineering case studies. As the experiment results, the AVOA algorithm in 30 benchmarks out of 36 benchmarks has achieved better results than the optimizer algorithms and has a significant and better performance on the majority of engineering problems. Wilcoxon rank-sum test has been used for statistical evaluation and indicates the significant superiority of the AVOA algorithm at a 95% confidence interval.