Butterfly optimization algorithm: a novel approach for global optimization
Butterfly optimization algorithm: a novel approach for global optimization
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DOI：
10.1007/s00500-018-3102-4
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发表时间：
2019-02-01
期刊：
影响因子：
4.1
通讯作者：
Singh, Satvir
中科院分区：
文献类型：
--
作者：
Arora, Sankalap;Singh, Satvir
现实世界的问题是复杂的，因为它们本质上是多维和多模态的，这鼓励计算机科学家开发更好和更有效的解决问题的方法。自然启发的元分析方法表现出比传统方法更好的性能。到目前为止，研究人员已经提出并实验了各种自然启发式算法来处理各种搜索问题。本文介绍了一种新的自然启发式算法，即蝴蝶优化算法（BOA），模仿蝴蝶的食物搜索和交配行为，以解决全局优化问题。该框架主要基于蝴蝶的觅食策略，利用它们的嗅觉来确定花蜜或交配伙伴的位置。在本文中，所提出的算法进行了测试和验证的一组30个基准测试函数，其性能与其他元启发式算法进行了比较。BOA还用于解决三个经典的工程问题（弹簧设计，焊接梁设计和齿轮系设计）。实验结果表明，该算法比其他元启发式算法更有效。
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.