Wang Heng, Zhang Zhuhong
College of Big Data and Information Engineering, Guizhou University, Guiyang, Guizhou 550025, P.R. China.
Tongren Polytechnic College, Tongren, Guizhou 554300, P.R. China.
iScience. 2024 Jan 29;27(3):109040. doi: 10.1016/j.isci.2024.109040. eCollection 2024 Mar 15.
Biological visual systems intrinsically include multiple kinds of motion-sensitive neurons. Some of them have been successfully used to construct neural computational models for problem-specific engineering applications such as motion detection, object tracking, etc. Nevertheless, it remains unclear how these neurons' response mechanisms can be contributed to the topic of optimization. Hereby, the dragonfly's visual response mechanism is integrated with the inspiration of swarm evolution to develop a dragonfly visual evolutionary neural network for large-scale global optimization (LSGO) problems. Therein, a grayscale image input-based dragonfly visual neural network online outputs multiple global learning rates, and later, such learning rates guide a population evolution-like state update strategy to seek the global optimum. The comparative experiments show that the neural network is a competitive optimizer capable of effectively solving LSGO benchmark suites with 2000 dimensions per example and the design of an operational amplifier.
生物视觉系统本质上包含多种运动敏感神经元。其中一些已成功用于构建针对特定问题的工程应用（如运动检测、目标跟踪等）的神经计算模型。然而，这些神经元的响应机制如何有助于优化这一主题仍不清楚。据此，将蜻蜓的视觉响应机制与群体进化的启发相结合，开发了一种用于大规模全局优化（LSGO）问题的蜻蜓视觉进化神经网络。其中，基于灰度图像输入的蜻蜓视觉神经网络在线输出多个全局学习率，随后，这些学习率指导类似种群进化的状态更新策略以寻找全局最优解。对比实验表明，该神经网络是一种有竞争力的优化器，能够有效解决每个示例具有2000维的LSGO基准测试集以及运算放大器的设计问题。
