SOM-Guided Evolutionary Search for Solving MinMax Multiple-TSP
This work addresses a practical problem in vehicle routing for logistics and transportation, though it appears incremental as it combines existing methods.
The paper tackled the MinMax formulation of the Single-Depot Multiple-TSP by developing a hybrid approach using Self-Organizing Maps, Evolutionary Algorithms, and Ant Colony Systems, resulting in significant improvements that outperformed existing literature results on TSPLIB instances.
Multiple-TSP, also abbreviated in the literature as mTSP, is an extension of the Traveling Salesman Problem that lies at the core of many variants of the Vehicle Routing problem of great practical importance. The current paper develops and experiments with Self Organizing Maps, Evolutionary Algorithms and Ant Colony Systems to tackle the MinMax formulation of the Single-Depot Multiple-TSP. Hybridization between the neural network approach and the two meta-heuristics shows to bring significant improvements, outperforming results reported in the literature on a set of problem instances taken from TSPLIB.