Enhanced adaptive-convergence in Harris’ hawks optimization algorithm
Mingxuan Mao, D. Gui
Abstract
Abstract This paper presents a novel enhanced adaptive-convergence in Harris’ hawks optimization algorithm (EAHHO). In EAHHO, considering that Harris’ hawks will adopt different perching strategies and chasing styles according to the value of the escaping energy parameter E, nonlinear adaptive-convergence factor a is designed and adjusted to enhance the convergence and robustness of the algorithm. Moreover, the convergence and stability of EAHHO are proved mathematically by using the Markov chain theory and Lyapunov stability theory respectively. Moreover, numerical simulation results of 14 HHOs with different nonlinear convergence factors on 23 benchmark functions show that the nonlinear convergence factor of EAHHO is applicable to challenging problems with unknown search spaces, and the comparisons with the selected well-established algorithms on 56 test functions demonstrate that EAHHO performs competitively and effectively. Finally, the experiment results show that EAHHO algorithm also has a good performance to solve the optimization problems with relatively high dimensions and graph size of Internet of Vehicles routing problem.