Solving Dynamic Many-objective TSP using NSGA-III equipped with SVR-RBF Kernel Predictor
Rashi Gupta, Satyasai Jagannath Nanda · 2021
Dynamic multi-objective TSP (DMTSP) finds extensive applications in scheduling and routing problems. The task is challenging due to the change in problem environment (arrangement and number of cities) after certain time period. To solve this, in this manuscript a new prediction based dynamic multi-objective optimization method termed as Dynamic non-dominated sorting genetic algorithm III (DNSGA-III) is proposed. This approach reuses the information obtained from previous Pareto optimal sets (POS) to train prediction models. The prediction has been carried out with SVR-RBF, SVR-Linear, polynomial interpolation and cubic spline based prediction approaches and to determine new solutions that are closer to the reference points. This significantly promote population diversity, along with desired convergence. Performance of the proposed DNSGA-III approach has been validated on four benchmark JY test problems. Further a sixteen cities DMTSP problem with two objective functions is solved using the proposed algorithm.