Grey Wolf Optimization Algorithm for a Concurrent Real-Time Optimization Problem in Game Theory

This paper presents a grey wolf algorithm for a concurrent real-time optimization problem in searching for an optimal game-solving solution. There are many solutions to the game. Each solution can demand different optimal values of different parameters. However, some ways the players try to solve the game do not lead to success. The optimization problem consists of two phases. Each phase impacts the second one in real time. The first phase is responsible for the optimization of the parameters. The second phase validates the choice and optimizes the parameters. As an optimization method, we chose grey wolf optimization. At the beginning, the algorithm generates several solutions. The solution with the value of the parameters closest to maximum is the position of an alpha wolf. The rest of the solutions are, according to the values of the parameters, split into the positions of beta, delta, and omega wolves.