Solving Sequential Competitive Facility Location Challenges: Using Parallel Genetic Algorithms

In smart computing, addressing combinatorial optimization game theory problems is essential for resolving intricate challenges involving limited resources and significant strategic interactions among decision-makers. In this paper, we solved sequential combinatorial optimization game theory problems using an evolutionary algorithm called the Game-Theoretic Genetic Algorithm (GTGA), where it is computationally infeasible to enumerate all players' decision options. The computational experiments show that the proposed fitness evaluation methods enable the GTGA to converge to true Stackelberg equilibria. Our research highlights the practical applicability of the GTGA in addressing real-world problems characterized by dynamic decision-making and strategic interactions.