Decision-making of multi-UAV combat game via enhanced competitive learning pigeon-inspired optimization

Decision-making of multi-unmanned aerial vehicle (multi-UAV) combat game is a crucial problem in the field of unmanned aerial vehicle combat game. In this study, an enhanced competitive learning pigeon-inspired optimization (ECPIO) algorithm is proposed to handle decision-making of multi-UAV combat game. Firstly, a six degree of freedom UAV model is adopted and situation assessment between UAVs is designed, the payment matrixes corresponding to two players in combat game are calculated. Then, non-cooperative game model is selected, the problem of multi-UAV cambat game decision-making is transformed into optimization based on the mixed Nash equilibrium, and ECPIO is adopted to calculate the optimal solution. ECPIO preserves the obvious advantage of pigeon-inspired optimization (PIO), which has fast convergence rate. Our proposed ECPIO can reduce the probability of optimization results trapping into local optimum by introducing enhanced competitive learning strategy. Finally, ECPIO is compared with the basic PIO, basic particle swarm optimization (PSO), competitive particle swarm optimization (CSO) and opposition-based learning particle swarm optimization (OBPSO) by a series of comparative simulation experiments, and the experimental results verify the feasibility and superiority in solving the decision-making of multi-UAV combat game problem. © 2024 Chinese Academy of Sciences. All rights reserved.