A rebuilt clone elastic net algorithm for traveling salesman problem

In this paper, we analyze the relationships between solution qualities and the number of initial dynamic points in elastic net algorithm, and propose a rebuilt clone elastic network algorithm(ReBCEN) for traveling salesman problem(TSP) according to the analysis results. For solving traveling salesman problem, firstly our algorithm initializes less dynamic points to produce a general accessing path of solution which is similar with that of optimal solutions. Then by using our novel parameter tuning strategies and nearest neighbor method, our algorithm performs a rebuilt cloning process to insert cloned dynamic points into the rubber band of elastic net and adjust accessing path to find the optimal solutions. As it utilizes less dynamic points in every epoch and unmatched cities in TSP are reduced gradually, ReBCEN can obtain faster convergence speed than the original elastic net and some other modified elastic nets. ReBCEN was simulated on some random TSPs and TSPLIB instances to verify its performance by comparison with other algorithms.