A whale optimization algorithm based on probabilistic elite difference mutation and adaptive golden sine was proposed to solve the shortcomings of low convergence speed and low optimization accuracy.The Latin hypercube sampling based on the idea of maximum and minimum was optimized to initialize the whale population,which made the initial population distribution more uniform with better global search ability.The golden sine algorithm with cosine adaptive operator was proposed to improve the spiral updating of whale,so as to accelerate the convergence speed and improve the convergence accuracy.The probabilistic elite difference variation method was designed and the greedy selection was carried out to optimize the algorithm flow and enhance the ability of the algorithm to jump out of the local optimal.Five single-peak test functions,five multi-peak test functions and five multi-modal test functions with multiple optimal solutions were selected to conduct comparison experiments with the main-stream optimization algorithms.Experimental results show that the proposed algorithm has higher optimization accuracy,higher convergence speed and better global search ability,and the effectiveness of the improved strategy of the algorithm is verified by the ablation experiment.
维普
万方数据