Plant intelligence based metaheuristic optimization algorithms

被引:169
作者
Akyol, Sinem [1 ]
Alatas, Bilal [2 ]
机构
[1] Tunceli Univ, Dept Comp Engn, Tunceli, Turkey
[2] Firat Univ, Dept Software Engn, Elazig, Turkey
关键词
Plant intelligence; Global optimization; Metaheuristic methods; INVASIVE WEED OPTIMIZATION; IMPERIALIST COMPETITIVE ALGORITHM; FLOWER POLLINATION ALGORITHM; LEARNING-BASED OPTIMIZATION; SOLVE TOY MODEL; NONSMOOTH; DISPATCH; DESIGN;
D O I
10.1007/s10462-016-9486-6
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Classical optimization algorithms are insufficient in large scale combinatorial problems and in nonlinear problems. Hence, metaheuristic optimization algorithms have been proposed. General purpose metaheuristic methods are evaluated in nine different groups: biology-based, physics-based, social-based, music-based, chemical-based, sport-based, mathematics-based, swarm-based, and hybrid methods which are combinations of these. Studies on plants in recent years have showed that plants exhibit intelligent behaviors. Accordingly, it is thought that plants have nervous system. In this work, all of the algorithms and applications about plant intelligence have been firstly collected and searched. Information is given about plant intelligence algorithms such as Flower Pollination Algorithm, Invasive Weed Optimization, Paddy Field Algorithm, Root Mass Optimization Algorithm, Artificial Plant Optimization Algorithm, Sapling Growing up Algorithm, Photosynthetic Algorithm, Plant Growth Optimization, Root Growth Algorithm, Strawberry Algorithm as Plant Propagation Algorithm, Runner Root Algorithm, Path Planning Algorithm, and Rooted Tree Optimization.
引用
收藏
页码:417 / 462
页数:46
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