标杆管理
感知器
计算机科学
人工智能
机器学习
启发式
经济短缺
人口
稳健性(进化)
数学优化
人工神经网络
数学
生物化学
人口学
化学
营销
政府(语言学)
社会学
业务
基因
语言学
哲学
操作系统
作者
Aala Kalananda Vamsi Krishna Reddy,Venkata Lakshmi Narayana Komanapalli
标识
DOI:10.1007/s11042-023-15146-x
摘要
This article presents a competitive learning-based Grey Wolf Optimizer (Clb-GWO) formulated through the introduction of competitive learning strategies to achieve a better trade-off between exploration and exploitation while promoting population diversity through the design of difference vectors. The proposed method integrates population sub-division into majority groups and minority groups with a dual search system arranged in a selective complementary manner. The proposed Clb-GWO is tested and validated through the recent CEC2020 and CEC2019 benchmarking suites followed by the optimal training of multi-layer perceptron’s (MLPs) with five classification datasets and three function approximation datasets. Clb-GWO is compared against the standard version of GWO, five of its latest variants and two modern meta-heuristics. The benchmarking results and the MLP training results demonstrate the robustness of Clb-GWO. The proposed method performed competitively compared to all its competitors with statistically significant performance for the benchmarking tests. The performance of Clb-GWO the classification datasets and the function approximation datasets was excellent with lower error rates and least standard deviation rates.
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