This paper presents a novel and an improved cuckoo search algorithm (ISCS), different from other techniques, which sets nonlinear decreasing inertia weight and adaptive scaling factor. As the iteration goes on, these two parameters are controlled by two functions to change the iterations dynamically. At the beginning of the iteration, the values of the two parameters are favorable for global search, and at the end of the iteration, their values are more favorable for local optimization. In this work, 23 classical benchmark functions are selected to improve the accuracy and convergence speed by conducting the simulation experiments on CS, ISCS and other three algorithms. The results show that the improved cuckoo algorithm enhances the accuracy of understanding and speeds up the convergence of the curves. Finally, ISCS is applied to probabilistic neural network (PPN) neural network classification and recognition technology, and the results show that the optimization of ISCS can effectively improve the classification accuracy of the test sets, and has diverse applications.
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