Development of Ant Lion Optimizer toolkit in LabVIEW™

Shifali Gupta, Vineet Kumar, K. Rana, P. Mishra, J. Kumar
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引用次数: 6

Abstract

Metaheuristic or nature inspired optimization have evolved over time towards development of a general purpose optimizer. In this paper, a novel nature inspired algorithm, based on hunting mechanism of antlions, Ant Lion Optimizer toolkit is developed in LabVIEWTM environment. This paper provides an orderly procedure for development of toolkit including random walk of ants, building of traps, catching of prey and finally rebuilding of traps. LabVIEWTM is a highly productive development environment used largely in measurement and control applications. The only standard optimizer toolkit available in LabVIEW™ is Differential Evolution (DE) and therefore a need was felt to have an efficient optimizer. The toolkit so evolved in this paper when tested on different benchmark functions has performed adequately well over the DE toolkit. The results so obtained clearly prove that the submitted toolkit is able to provide superior results in terms of improved exploration, local optima avoidance, exploitation, and convergence.
Ant Lion优化工具箱在LabVIEW™中的开发
随着时间的推移,元启发式优化或自然启发优化已经发展成为通用优化器。本文在LabVIEWTM环境下,基于蚁群的捕猎机制,开发了一种新颖的自然启发算法——蚁群优化器工具箱。本文给出了蚁群随机行走、设置陷阱、捕获猎物、重建陷阱的开发流程。LabVIEWTM是一个高效的开发环境,主要用于测量和控制应用。LabVIEW™中唯一可用的标准优化器工具包是差分进化(Differential Evolution, DE),因此需要一个高效的优化器。在对不同的基准函数进行测试时,本文中发展的工具包比DE工具包执行得更好。结果清楚地证明,所提交的工具包能够在改进的勘探、局部最优避免、开发和收敛方面提供优越的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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