Butterfly optimization algorithm: a novel approach for global optimization

被引:1249
作者
Arora, Sankalap [1 ]
Singh, Satvir [1 ]
机构
[1] IK GUJRAL Punjab Tech Univ, Jalandhar, Punjab, India
关键词
Butterfly optimization algorithm; Global optimization; Nature inspired; Metaheuristic; Benchmark test functions; Engineering design problems; PARTICLE SWARM OPTIMIZATION; ENGINEERING OPTIMIZATION; STRUCTURAL OPTIMIZATION; DIFFERENTIAL EVOLUTION; SEARCH ALGORITHM; OPTIMAL-DESIGN; PERFORMANCE; INTEGER;
D O I
10.1007/s00500-018-3102-4
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Real-world problems are complex as they are multidimensional and multimodal in nature that encourages computer scientists to develop better and efficient problem-solving methods. Nature-inspired metaheuristics have shown better performances than that of traditional approaches. Till date, researchers have presented and experimented with various nature-inspired metaheuristic algorithms to handle various search problems. This paper introduces a new nature-inspired algorithm, namely butterfly optimization algorithm (BOA) that mimics food search and mating behavior of butterflies, to solve global optimization problems. The framework is mainly based on the foraging strategy of butterflies, which utilize their sense of smell to determine the location of nectar or mating partner. In this paper, the proposed algorithm is tested and validated on a set of 30 benchmark test functions and its performance is compared with other metaheuristic algorithms. BOA is also employed to solve three classical engineering problems (spring design, welded beam design, and gear train design). Results indicate that the proposed BOA is more efficient than other metaheuristic algorithms.
引用
收藏
页码:715 / 734
页数:20
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