Dragonfly visual systems intrinsically incorporate a variety of motion‐sensitive neurons able to be well contributed to probe into bio‐inspired computational models. However, it remains unclear how their visual response mechanisms can be borrowed to construct neurocomputational models for solving optimization problems. Hereby, a feedforward dragonfly visual attention–merged neural network (DVAMNN) with presynaptic and postsynaptic subnetworks is developed to output two types of online activities named learning rates in terms of the dragonfly visual information‐processing and attention mechanisms. Integrated such learning rates into a new‐type and metaheuristics‐inspired state transition strategy, a dragonfly visual attention–merged evolutionary neural network (DVAMENN) with the unique parameter of input resolution is developed to solve ultrahigh dimensional global optimization (UHDGO) problems. The theoretical analysis implicates that the DVAMENN’s complexity is mainly decided by the optimization problem itself. Experimental results have confirmed that DVAMENN can successfully optimize the structures of two sixth‐order active filters and discover the global or approximate solutions of the CEC’ 2010 and CEC’ 2013 benchmark suites with dimension 20,000 per example. Nevertheless, the compared metaheuristics encounter unprecedented troubles in the case of UHDGO.

Biological systems have a great number of visual motion detection neurons, some of which can preferentially react to specific visual regions. Nevertheless, little work has been performed about how they can be used to develop neural network models for omnidirectional collision detection. Hereby, an artificial fly visual brain neural network with presynaptic and postsynaptic subnetworks, for the first time, is developed to detect the changes of visual motion in panoramic scenes. Herein, the presynaptic subnetwork, which originates from the preferential response characteristics of five fly visual neurons, responds to all the moving objects in the panoramic field; the postsynaptic network, which is based on the properties of the angle and height detection neurons in the fly’s brain system, collects the excitatory intensities of the visual neurons, and outputs the real‐time activities of the main object closest to the panoramic camera. Hereafter, a computational model is constructed to implement omnidirectional collision detection, relying upon the artificial visual brain neural network and three functional neurons. The theoretical analysis has verified that the collision detection model’s computational complexity depends mainly on the image input resolution. Three experimental conclusions can be clearly drawn: (i) the motion characteristics of the main object in the panoramic environment can be clearly exhibited in the neural network; (ii) the collision detection model can not only outperform the compared models but also successfully perform omnidirectional collision detection; and (iii) it spends 0.24 s or so to execute visual information processing per frame with the resolution of 120 × 80.

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.