Neurophysiologic achievements claimed that the fly visual system could naturally contribute to a type of artificial computation model which used motion-sensitive neurons to detect the local movement direction changes of moving objects. It, however, still remains open how the neurons' information-processing mechanisms and the inspirations of swarm intelligence can be integrated to serve an interdisciplinary topic between computer vision and intelligence optimization-visual evolutionary neural networks. Hereby, a fly visual evolutionary neural network is developed to solve large-scale global optimization (LSGO), inspired by swarm evolution and the characteristics of fly visual perception. It includes two functional modules, of which one is to generate global and local motion direction activities of visual neural nodes, and the other takes the activities as learning rates to update the nodes' states by a population-like evolutionary strategy. Also, it is used to optimize the structure of a multilayer perceptron to acquire a sample classification model. The theoretical results indicate that the network is convergent and meanwhile the computational complexity mainly depends on the size of the input layer and the dimension of LSGO. The comparative experiments have verified that the network is an extremely competitive optimizer for LSGO problems.

中文翻译：

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飞行视觉进化神经网络求解大规模全局优化

神经生理学成果声称苍蝇视觉系统可以自然地促进一种人工计算模型，该模型使用运动敏感神经元来检测运动物体的局部运动方向变化。然而，如何整合神经元的信息处理机制和群体智能的灵感，以服务于计算机视觉和智能优化 - 视觉进化神经网络之间的跨学科主题仍然是开放的。因此，受群体进化和苍蝇视觉感知特性的启发，开发了一种苍蝇视觉进化神经网络来解决大规模全局优化（LSGO）问题。它包括两个功能模块，其中一个是生成视觉神经节点的全局和局部运动方向活动，另一个将活动作为学习率，通过类似种群的进化策略来更新节点的状态。此外，它用于优化多层感知器的结构以获得样本分类模型。理论结果表明网络是收敛的，同时计算复杂度主要取决于输入层的大小和LSGO的维数。对比实验已经证实，该网络是一个极具竞争力的 LSGO 问题优化器。理论结果表明网络是收敛的，同时计算复杂度主要取决于输入层的大小和LSGO的维数。对比实验已经证实，该网络是一个极具竞争力的 LSGO 问题优化器。理论结果表明网络是收敛的，同时计算复杂度主要取决于输入层的大小和LSGO的维数。对比实验已经证实，该网络是一个极具竞争力的 LSGO 问题优化器。