Root system growth for global optimization

Hanning Chen, Xiaodan Liang, Weitao Yuan, Liling Sun, Maowei He, Nuo Ji
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Abstract

This paper proposed a novel bio-inspired optimizer, namely the root system growth algorithm (RSGA), which adopts the root foraging, memory and communication, and auxin-regulated mechanisms of the root system. When tested against benchmark functions, the RSGA markedly outperforms the CMA-ES, PSO, GA, and DE algorithms in terms of accuracy, robustness and convergence speed.
根系生长实现全局优化
本文提出了一种新的仿生优化器,即根系生长算法(RSGA),该算法利用根系的觅食、记忆和通讯以及生长素调控机制。在对基准函数进行测试时,RSGA在精度、鲁棒性和收敛速度方面明显优于CMA-ES、PSO、GA和DE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。
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