A grey wolf optimizer using Gaussian estimation of distribution and its application in the multi-UAV multi-target urban tracking problem

Abstract

To overcome premature convergence in the grey wolf optimizer (GWO), in this study, a modified GWO integrating the basic GWO with the Gaussian estimation of distribution (GED) strategy, called GEDGWO, is proposed. GEDGWO employs the Gauss probability model to estimate the distribution of the selected superior individuals and shifts the weighted mean to adjust the search directions. Additionally, a Gaussian distribution based inferior solutions repair (ISR) method is introduced to modify the ill-shaped distribution of the population. A disturbed Gaussian random walk method is utilized to strengthen the local exploration ability. The performance of GEDGWO is compared with those of other promising GWO variants and state-of-the-art algorithms on a benchmarking CEC 2014 test suite. Non-parametric Wilcoxon and Friedman tests as well as the post hoc Iman–Davenport test are performed to further verify the efficacy of GEDGWO. Moreover, GEDGWO is applied to solve multi-UAV multi-target urban tracking path planning problem. To overcome the shortcomings of the previous solution model, a new model is described to address this complex real-time engineering optimization problem. The validity and practicability of the problem models as well as the accuracy and efficiency of GEDGWO are demonstrated by the experimental results.