A hybrid improved whale optimization algorithm

Aiming at the problems that whale optimization algorithm is easy to fall into local optimal, slow convergence speed and low accuracy in the complex environment of multi-variable processing, it has always been challenging in practical application. In this paper, a new hybrid modified whale optimization algorithm (HIWOA) is proposed, which adds a new feedback mechanism to improve population diversity and reduce the possibility of falling into local optimization. The nonlinear convergence factor and inertia weight coefficient are used to improve the updating of whale individual position and improve the speed of convergence and accuracy. Simulation experiments were carried out on 23 benchmark functions, and the results showed that compared with the original WOA algorithm and the other three improved algorithms in the last two years, the HIWOA algorithm was more competitive in accuracy of solution, convergence speed and stability.