Due to the shortcomings of the Harris Hawks optimization(HHO)algorithm,such as premature convergence,low optimization precision,and slow convergence speed,an improved HHO(IHHO)algorithm integrating nonlinear convergence factor and mutation quasi-reflection-based learning(QRBL)is proposed.First,circle chaotic mapping is in-troduced in the initialization stage to improve the diversity of the initialization population and the location and quality of the population.Second,the sigmoid nonlinear convergence factor is introduced to balance the ability of global explora-tion and partial exploitation.Finally,because the HHO algorithm easily falls into the local optimum,mutation QRBL is proposed to improve the vigor of the population and further improve the local convergence ability of the algorithm.The simulation experiments are conducted by applying 13 standard test functions and one classical engineering problem to the evaluation of the proposed algorithm.The results show that the convergence accuracy and the convergence speed of the IHHO algorithm are greatly improved,and IHHO is suitable for solving practical problems.
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