Improved grey wolf optimizer for multiple unmanned aerial vehicles task allocation

Yu Wang, Qifang Luo, Yongquan Zhou


Grey wolf optimizer (GWO) is a metaheuristic optimization algorithm proposed in 2014, which has already been applied in many fields. However, there are still two problems in GWO: i) during the optimization process, there are three leading wolves to lead the population for search, resulting in poor population diversity and ii) because of its position updated equation which not only brings strong convergence ability but also makes it easily fall into local optimal. In this paper, to overcome this, the following contributions were made: i) an improved GWO (IGWO) with two strategies was proposed to solve the above problems and ii) for verifying the effectiveness of IGWO, it was applied in solving multiple UAVs task allocation problems. The experimental results show that IGWO can solve this problem well and suit for large-scale complex examples.


Congestion control strategy; Global best search strategy; Grey wolf optimizer; Improve grey wolf optimization; Metaheuristic optimization; Multiple UAVs task allocation

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