元启发式
水准点(测量)
算法
觅食
计算机科学
优化算法
进化算法
威尔科克森符号秩检验
数学优化
生物
人工智能
数学
统计
地理
生态学
大地测量学
曼惠特尼U检验
生物
考古
自然(考古学)
作者
Benyamın Abdollahzadeh,Farhad Soleimanian Gharehchopogh,Seyedali Mirjalili
标识
DOI:10.1016/j.cie.2021.107408
摘要
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.
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