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

被引:953
作者
Abdollahzadeh, Benyamin [1 ]
Gharehchopogh, Farhad Soleimanian [1 ]
Mirjalili, Seyedali [2 ,3 ]
机构
[1] Islamic Azad Univ, Dept Comp Engn, Urmia Branch, Orumiyeh, Iran
[2] Torrens Univ Australia, Ctr Artificial Intelligence Res & Optimisat, Brisbane, Qld 4006, Australia
[3] Yonsei Univ, Yonsei Frontier Lab, Seoul, South Korea
关键词
Metaheuristic; Algorithm; Artificial vulture optimization algorithm; African vultures; Optimization; Artificial Intelligence; Benchmark; Soft Computing; PARTICLE SWARM OPTIMIZATION; ENGINEERING OPTIMIZATION; DIFFERENTIAL EVOLUTION; SEARCH ALGORITHM; DESIGN OPTIMIZATION; STRUCTURAL OPTIMIZATION; HEURISTIC OPTIMIZATION; NECROSYRTES-MONACHUS; FORAGING SUCCESS; LEVY FLIGHTS;
D O I
10.1016/j.cie.2021.107408
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
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.
引用
收藏
页数:37
相关论文
共 134 条
[61]   A novel heuristic optimization method: charged system search [J].
Kaveh, A. ;
Talatahari, S. .
ACTA MECHANICA, 2010, 213 (3-4) :267-289
[62]   Vibrating particles system algorithm for truss optimization with multiple natural frequency constraints [J].
Kaveh, Ali ;
Ghazaan, Majid Ilchi .
ACTA MECHANICA, 2017, 228 (01) :307-322
[63]   MECHANISMS OF COEXISTENCE IN VULTURES: UNDERSTANDING THE PATTERNS OF VULTURE ABUNDANCE AT CARCASSES IN MASAI MARA NATIONAL RESERVE, KENYA [J].
Kendall, Corinne ;
Virani, Munir Z. ;
Kirui, Paul ;
Thomsett, Simon ;
Githiru, Mwangi .
CONDOR, 2012, 114 (03) :523-531
[64]   OPTIMIZATION BY SIMULATED ANNEALING [J].
KIRKPATRICK, S ;
GELATT, CD ;
VECCHI, MP .
SCIENCE, 1983, 220 (4598) :671-680
[65]   Glowworm swarm optimization for simultaneous capture of multiple local optima of multimodal functions [J].
Krishnanand K.N. ;
Ghose D. .
Swarm Intelligence, 2009, 3 (2) :87-124
[66]  
Kumar N., 2021, SOFT COMPUT, P1
[67]   A new structural optimization method based on the harmony search algorithm [J].
Lee, KS ;
Geem, ZW .
COMPUTERS & STRUCTURES, 2004, 82 (9-10) :781-798
[68]  
Liang J. J., 2013, PROBLEM DEFINITIONS, V635, P490, DOI DOI 10.1109/CEC.2014.6900489
[69]   Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization [J].
Liu, Hui ;
Cai, Zixing ;
Wang, Yong .
APPLIED SOFT COMPUTING, 2010, 10 (02) :629-640
[70]   A survey on applications of the harmony search algorithm [J].
Manjarres, D. ;
Landa-Torres, I. ;
Gil-Lopez, S. ;
Del Ser, J. ;
Bilbao, M. N. ;
Salcedo-Sanz, S. ;
Geem, Z. W. .
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2013, 26 (08) :1818-1831