Dragonfly Visual Attention–Merged Evolutionary Neural Network Solving Ultrahigh Dimensional Global Optimization Problems

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.