Agent-based Optimized Genetic Algorithm for Solving TSP Problems

In this study, an ABM framework is proposed to optimize the genetic algorithm for solving the traveler problem, and a new heuristic crossover and stochastic mutation are proposed to improve the genetic algorithm. Meanwhile, this paper applies a novel TSP solution to the NetLogo simulation platform and constructs the NGA-T model, which contains key steps such as crossover, mapping and diversity assessment. The experimental results show that the algorithm can effectively find the solution closest to the optimal solution and outperforms the traditional genetic algorithm. The model has potential applications in solving real traveler's problems and can be used in planning travel routes, optimizing logistics and other fields.