Gorilla algorithm based on double random perturbation and its engineering application

Aiming at the problems of artificial gorilla troop optimization algorithm, such as easy to fall into local optimum, slow convergence speed and low optimization accuracy, an artificial gorilla troop optimization algorithm based on double random perturbation strategy is proposed. Firstly, the Halton sequence is introduced to initialize the population to increase the diversity of the population; secondly, a multi-dimensional random number strategy is used in the algorithm optimization stage and an adaptive position search mechanism is proposed in the exploration stage to improve the convergence speed of the algorithm; thirdly, a double random perturbation strategy is proposed to solve the group effect of gorillas and enhance the ability of the algorithm to jump out of the local optimum; finally, a dimension-by-dimensional update strategy is adopted to update individual positions, which improves the convergence accuracy of the algorithm. Through the comparison of the optimization results of 10 benchmark test functions and the Wilcoxon rank sum test, it can be seen that the improved algorithm has greatly improved the optimization accuracy and convergence speed. In addition, through the experimental comparative analysis of an engineering optimization problem, the superiority of the proposed algorithm in dealing with real engineering problems is further verified.