Autonomous Decision-Making for Air Gaming Based on Position Weight-Based Particle Swarm Optimization Algorithm.

As the complexity of air gaming scenarios continues to escalate, the demands for heightened decision-making efficiency and precision are becoming increasingly stringent. To further improve decision-making efficiency, a particle swarm optimization algorithm based on positional weights (PW-PSO) is proposed. First, important parameters, such as the aircraft in the scenario, are modeled and abstracted into a multi-objective optimization problem. Next, the problem is adapted into a single-objective optimization problem using hierarchical analysis and linear weighting. Finally, considering a problem where the convergence of the particle swarm optimization (PSO) is not enough to meet the demands of a particular scenario, the PW-PSO algorithm is proposed, introducing position weight information and optimizing the speed update strategy. To verify the effectiveness of the optimization, a 6v6 aircraft gaming simulation example is provided for comparison, and the experimental results show that the convergence speed of the optimized PW-PSO algorithm is 56.34% higher than that of the traditional PSO; therefore, the algorithm can improve the speed of decision-making while meeting the performance requirements. [ABSTRACT FROM AUTHOR]