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

• Published in: Computers & industrial engineering Vol. 158; p. 107408
• Main Authors: Abdollahzadeh, Benyamin, Gharehchopogh, Farhad Soleimanian, Mirjalili, Seyedali
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.08.2021
• Subjects: African vultures; Algorithm; Artificial Intelligence; Artificial vulture optimization algorithm; Benchmark; Metaheuristic; Optimization; Soft Computing; Metaheuristic; Artificial Intelligence; African vultures; Benchmark; Soft Computing; Artificial vulture optimization algorithm; Algorithm; Optimization
• ISSN: 0360-8352; 1879-0550
• DOI: 10.1016/j.cie.2021.107408

•A new metaheuristic algorithm is proposed inspired by African vultures’ lifestyle.•The proposed algorithm is first tested on 36 standard test functions.•Eleven engineering problems are then solved to ensure the applicability and black box nature.•Statistical results demonstrate the merits of the proposed algorithm. 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.