Non-Swarm Plant Intelligence Algorithm: BladderWorts Suction (BWS) Algorithm

R. Gowri, R. Rathipriya
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引用次数: 4

Abstract

BladderWorts Suction (BWS) Algorithm is proposed in this article, which is a novel Plant intelligence inspired meta-heuristic non-swarm algorithm. The concept of non-swarm intelligence is focused in this paper because of the autonomous behavior of the individuals. In the swarm intelligence algorithms, the individuals are influenced by the global knowledge. Sometimes, the small change by an individual may result in group level behavior. The core inspiration for this non-swarm optimization algorithm is the foraging suction mechanism of the aquatic plant Utricularia (Bladderworts). The worst bladders are regenerated periodically to accelerate the solution search of the stagnant bladders at local optima. It is tested with different benchmark test functions. The optimizing tendency of this algorithm is studied empirically, which yields good results. Comparatively, BWS algorithm also produces positive insights like various other popular optimization techniques.
非群体植物智能算法:BladderWorts Suction (BWS)算法
本文提出了一种基于植物智能的元启发式非群算法——BladderWorts Suction (BWS)算法。由于个体的自主行为,本文重点研究了非群体智能的概念。在群体智能算法中,个体受全局知识的影响。有时,个人的微小变化可能会导致群体层面的行为。该非群体优化算法的核心灵感来自水生植物水藻的觅食吸力机制。周期性地对最差的膀胱进行再生,以加速局部最优处停滞膀胱的解搜索。用不同的基准测试函数对其进行了测试。对该算法的优化倾向进行了实证研究,取得了较好的结果。相比之下,BWS算法与其他各种流行的优化技术一样,也产生了积极的见解。
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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