Parameter optimization of BP neural network based on coyote optimization algorithm with inverse time chaotic

A chaotic coyote optimization algorithm based on inverse time-decay operator(ICCOA) is proposed to solve the coyote optimization algorithm(COA), such as the poor performance and low diversity. Firstly, the inverse time decay weight factor is added in the process of individual iterative updating, so as to maintain the balance between global search and local development ability and improve the search speed of the algorithm. Secondly, add the chaotic interference mechanism based on Tent chaotic map, some poor individuals in the population were mapped to produce new individuals, thus increasing the diversity of the population. In order to verify the optimization ability of ICCOA algorithm, functional optimization tests were carried out in 10, 30 and 100 dimensions respectively, and compared with five optimization algorithms. The experimental results show that ICCOA algorithm has good optimization performance. Finally, the ICCOA algorithm is applied to the parameter optimization of BP neural network, and a new neural network model (ICCOABP) is proposed. Compared with the standard neural network and the BP neural network parameter optimization method based on genetic algorithm, the experimental results show that the ICCOABP algorithm is efficient.