Abstract
Aiming at the problems of the basic Harris Hawk optimization (HHO), such as insufficient global search ability, slow convergence speed, low convergence accuracy and easy to fall into local optimization, this paper proposes an improved HHO (IHHO) algorithm based on spiral search and neighborhood perturbation. First, Tent chaotic mapping is used to initialize the population, enhance the diversity of the population and improve the initial convergence speed of the proposed algorithm. Secondly, the collaborative updating strategy based on spiral search and neighborhood perturbation is used in the exploration stage to expand the search space and improve the global search ability of the proposed algorithm. In the exploitation stage, the adaptive inertia weight is introduced to improve the convergence speed. Then, the dimensional cross-variation is used to improve the ability of the algorithm to jump out of the local optimum. CEC2021 test function and 8 classic test functions are used to demonstrate the effectiveness of the proposed method. Compared with the basic HHO and the other improved swarm intelligence algorithms, the results show that the proposed algorithm has better global search ability, faster convergence speed and higher accuracy. Finally, the proposed IHHO algorithm is used to solve tension spring design problems.
Published Version
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