Improved Harris Hawks Optimization And Its Application

Harris Hawks Optimization(HHO)is a swarm intelligence algorithm that simulates the cooperative hunting behavior of Harris hawks.It has the advantages of simple principle,few parameters,and easy implementation.But it also has the problems of slow convergence speed,low convergence accuracy,and easy to fall into local optimum.Aiming at the shortcomings of Harris Hawks Optimization,this thesis proposes new schemes to improve Harris Hawks Optimization,and applies them to solve engineering design problems.The main work of this thesis is as follows:(1)A multi-population Harris Hawks Optimization(MHHO)is proposed.In MHHO,the division and cooperation strategy is introduced to divide individuals in the population into three categories,and different methods are used to optimize the population structure and enhance the ability of the algorithm to jump out of local optimum.The multi-population mechanism is introduced to divide the population into sub-populations,with each subpopulation working together to enhance the quality of the population and improve the convergence speed of the algorithm.Simulation experiments on 23 benchmark functions show that MHHO has certain improvement in optimization performance and robustness,and has certain competitiveness in the optimization algorithm.(2)An elite-guided Harris Hawks Optimization(EHHO)is proposed.In EHHO,the elite opposite learning is introduced,and the elite center is used as the symmetrical center for opposite learning to optimize the population structure and enhance the ability of the algorithm to jump out of local optimum.The elite evolution strategy is introduced,and the evolution based on Gaussian random mutation is carried out with elite individuals as the main body to improve the quality of the population and improve the convergence speed of the algorithm.The adaptive mechanism is introduced to dynamically adjust the selection probability of the two evolution modes in the elite evolution strategy to improve the stability of the algorithm.Simulation experiments on 23 benchmark functions show EHHO has obvious improvement in optimization performance and robustness,and has certain competitiveness in optimization algorithms.(3)Two improved algorithms are applied to solve seven engineering design problems.The experimental results show that the optimization performance of the two improved algorithms is improved compared to the original algorithm,providing a new and competitive solution for solving engineering design problems.