A novel nature-inspired algorithm for optimization: Squirrel search algorithm

被引:693
作者
Jain, Mohit [1 ]
Singh, Vijander [1 ]
Rani, Asha [1 ]
机构
[1] Univ Delhi, Netaji Subhas Inst Technol, Instrumentat & Control Engn Div, Delhi 110078, India
关键词
Nature-inspired algorithm; Unconstrained optimization; Squirrel search algorithm; PARTICLE SWARM OPTIMIZATION; DYNAMIC PARAMETER ADAPTATION; META-HEURISTIC OPTIMIZATION; SOUTHERN FLYING SQUIRREL; ANT COLONY OPTIMIZATION; TYPE-2; FUZZY-LOGIC; DIFFERENTIAL EVOLUTION; GLIDING PERFORMANCE; KRILL HERD; INTELLIGENCE;
D O I
10.1016/j.swevo.2018.02.013
中图分类号
TP18 [人工智能理论];
学科分类号
140502 [人工智能];
摘要
This paper presents a novel nature-inspired optimization paradigm, named as squirrel search algorithm (SSA). This optimizer imitates the dynamic foraging behaviour of southern flying squirrels and their efficient way of locomotion known as gliding. Gliding is an effective mechanism used by small mammals for travelling long distances. The present work mathematically models this behaviour to realize the process of optimization. The efficiency of the proposed SSA is evaluated using statistical analysis, convergence rate analysis, Wilcoxon's test and ANOVA on classical as well as modern CEC 2014 benchmark functions. An extensive comparative study is carried out to exhibit the effectiveness of SSA over other well-known optimizers in terms of optimization accuracy and convergence rate. The proposed algorithm is implemented on a real-time Heat Flow Experiment to check its applicability and robustness. The results demonstrate that SSA provides more accurate solutions with high convergence rate as compared to other existing optimizers.
引用
收藏
页码:148 / 175
页数:28
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