一种基于多维几何方法的进化优化算法:枢轴优化器

S. Thongkrairat, V. Chutchavong
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引用次数: 0

摘要

最近开发了许多优化技术。一些模拟自然活动或其他理论,例如灰狼优化器,它模拟狼狩猎机制以找到全局最小值,以及粒子群优化,它利用鸟类的群集行为来避免每个局部最小值。每个开发的算法都使用指导方针来改进其模仿并达到其目标。这项工作提出了枢轴优化器,这是一种新的进化优化算法,灵感来自多维几何方法,在每一代中创建一个独特的进化。这种模拟的目标是使算法适合于多情况问题并具有稳定的结果。结果表明,与其他竞争优化器相比,支点优化器在竞争问题上的表现更好。关键词优化,算法,群体智能,几何,GWO,粒子群算法
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Novel Evolution Optimization Algorithm Using a Multidimensional Geometric Method: Pivot Optimiser
Many optimisation techniques have recently been developed. Several mimic natural activity or another theory, such as the Grey Wolf Optimiser, which emulates wolf hunting mechanisms to find a global minimum, and Particle Swarm Optimisation, which uses birds’ flocking behaviour to avoid each local minimum. Each developed algorithm uses guidelines to improve its mimicry and reach its goal. This work proposes the pivot optimiser, a new evolution optimisation algorithm inspired by the multidimensional geometric method to create a unique evolution in each generation. The goal of this imitation is to make an algorithm suitable for a multi-situation problem with a stable result. The results show that the pivot optimiser outperformed on competitive problems compared with other competitive optimisers. Keywordsoptimisation, algorithm, swarm intelligence, geometric, GWO, PSO
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