Improved Grey Wolf Optimization Algorithm Based on Inertia Weights of Inverse Triangular Exponents

In this paper, an improved grey Wolf optimization algorithm based on inverse triangular power index inertia weight(ITGWO) is proposed to solve the problem of slow convergence and low accuracy of gray Wolf optimization algorithm(GWO).By constructing the inverse trigonometric power index function, the inertial weight is adjusted nonlinearly, and the solving precision and convergence speed of the algorithm are improved. Seven classical test functions are used to compare ITGWO with other 5 swarm intelligence algorithms. The experimental results show that ITGWO is superior to the other 5 algorithms in convergence speed and solving accuracy, which shows that ITGWO has stronger optimization ability for solving complex optimization problems.