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In this paper, we propose a two-stage optimization strategy for solving theLarge-scale Traveling Salesman Problems (LSTSPs) named CCPNRL-GA. First, wehypothesize that the participation of a well-performed individual as an elitecan accelerate the convergence of optimization. Based on this hypothesis, inthe first stage, we cluster the cities and decompose the LSTSPs into multiplesubcomponents, and each subcomponent is optimized with a reusable PointerNetwork (PtrNet). After subcomponents optimization, we combine all sub-tours toform a valid solution, this solution joins the second stage of optimizationwith GA. We validate the performance of our proposal on 10 LSTSPs and compareit with traditional EAs. Experimental results show that the participation of anelite individual can greatly accelerate the optimization of LSTSPs, and ourproposal has broad prospects for dealing with LSTSPs.

Accelerating the Genetic Algorithm for Large-scale Traveling Salesman  Problems by Cooperative Coevolutionary Pointer Network with Reinforcement  Learning