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Soft Computing

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08.03.2018 | Foundations

Butterfly optimization algorithm: a novel approach for global optimization

verfasst von: Sankalap Arora, Satvir Singh

Erschienen in: Soft Computing | Ausgabe 3/2019

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Abstract

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.

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Titel: Butterfly optimization algorithm: a novel approach for global optimization
verfasst von: Sankalap Arora
Satvir Singh
Publikationsdatum: 08.03.2018
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 3/2019
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-018-3102-4

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