Dynamic TSP Optimization Base on Elastic Adjustment

Prompt adaptation to environment is the key and difficult point of dynamic TSP optimization. The paper combines the Genetic Algorithm (GA) and an elastic adjusting method based on synapse intensifying mechanism in biological neural network aiming at elastic adjustment to the edge neighboring dynamic node, which will strengthen the exploration and exploitation to new local optimal area and accelerate the approaching to new optimization space for all. To dynamic TSP problems (DTSP) under regular or stochastic condition, the paper makes comparison between the elastic adjusting method and other three algorithms through experiments and research. The results represent that the new algorithm put forward holds fine dynamic adaptability. In the meanwhile, the influence of adjusting rate r on optimizing performance is also analyzed in the paper.