Nature inspired optimization algorithms or simply variations of metaheuristics?

被引:98
作者
Tzanetos, Alexandros [1 ]
Dounias, Georgios [1 ]
机构
[1] Univ Aegean, Sch Engn, Dept Financial & Management Engn, Management & Decis Engn Lab, 41 Kountouriotou Str, Chios 82132, Greece
关键词
Nature-inspired intelligent (NII) algorithms; Guidelines for nature-inspired algorithms; AI and optimization; Evaluation of algorithm's innovation; GLOBAL OPTIMIZATION; SWARM OPTIMIZATION; SEARCH; COLONY;
D O I
10.1007/s10462-020-09893-8
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In the last decade, we observe an increasing number of nature-inspired optimization algorithms, with authors often claiming their novelty and their capabilities of acting as powerful optimization techniques. However, a considerable number of these algorithms do not seem to draw inspiration from nature or to incorporate successful tactics, laws, or practices existing in natural systems, while also some of them have never been applied in any optimization field, since their first appearance in literature. This paper presents some interesting findings that have emerged after the extensive study of most of the existing nature-inspired algorithms. The need for irrationally introducing new nature inspired intelligent (NII) algorithms in literature is also questioned and possible drawbacks of NII algorithms met in literature are discussed. In addition, guidelines for the development of new nature-inspired algorithms are proposed, in an attempt to limit the misleading appearance of variation of metaheuristics as nature inspired optimization algorithms.
引用
收藏
页码:1841 / 1862
页数:22
相关论文
共 155 条
[1]  
Abbass HA, 2001, IEEE C EVOL COMPUTAT, P207, DOI 10.1109/CEC.2001.934391
[2]   Electromagnetic field optimization: A physics-inspired metaheuristic optimization algorithm [J].
Abedinpourshotorban, Hosein ;
Shamsuddin, Siti Mariyam ;
Beheshti, Zahra ;
Jawawi, Dayang N. A. .
SWARM AND EVOLUTIONARY COMPUTATION, 2016, 26 :8-22
[3]  
Abu Khurma R, 2020, ALGO INTELL SY, P131, DOI 10.1007/978-981-32-9990-0_8
[4]   Grenade Explosion Method-A novel tool for optimization of multimodal functions [J].
Ahrari, Ali ;
Atai, Ali A. .
APPLIED SOFT COMPUTING, 2010, 10 (04) :1132-1140
[5]   A novel bee swarm optimization algorithm for numerical function optimization [J].
Akbari, Reza ;
Mohammadi, Alireza ;
Ziarati, Koorush .
COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION, 2010, 15 (10) :3142-3155
[6]   Sports inspired computational intelligence algorithms for global optimization [J].
Alatas, Bilal .
ARTIFICIAL INTELLIGENCE REVIEW, 2019, 52 (03) :1579-1627
[7]  
Alauddin M, 2016, 2016 INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONICS, AND OPTIMIZATION TECHNIQUES (ICEEOT), P79, DOI 10.1109/ICEEOT.2016.7754783
[8]   Nature Inspired Optimization Algorithms Related to Physical Phenomena and Laws of Science: A Survey [J].
Alexandros, Tzanetos ;
Georgios, Dounias .
INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS, 2017, 26 (06)
[9]  
Ali J., 2015, Sci. Int., V27, P4939
[10]   Optimizing connection weights in neural networks using the whale optimization algorithm [J].
Aljarah, Ibrahim ;
Faris, Hossam ;
Mirjalili, Seyedali .
SOFT COMPUTING, 2018, 22 (01) :1-15