Artificial gorilla troops optimizer based on double random disturbance and its application of engineering problem

Traditional artificial gorilla troops optimizer (GTO) has the drawbacks of easily falling into local optimum, slow convergence speed, and low optimization accuracy. Aiming at these problems, an artificial gorilla troops optimizer based on a double random disturbance strategy (DGTO) was proposed. Firstly, the Halton sequence was introduced to initialize the population to increase the diversity of the population. Secondly, the method’s convergence speed was increased by using the multi-dimensional random number technique during the algorithm optimization stage and proposing an adaptive position exploration mechanism. Thirdly, a double random disturbance strategy was proposed, which solved the group effect of gorillas and enhanced the ability of the algorithm to jump out of the local optimum. Finally, the individual position was updated by a dimension-by-dimension update strategy, which improved the convergence accuracy of the algorithm. It is evident that the enhanced technique has a greater improvement in optimization accuracy and convergence speed when comparing the Wilcoxon rank sum test results with the optimization results of ten benchmark test functions. In addition, through the experimental comparative analysis of one practical engineering optimization problem, the superiority of the proposed algorithm in dealing with practical engineering problems is further verified.