ITO algorithm with local search for large scale multiple balanced traveling salesmen problem

In the fields such as computer science, intelligent transport systems and logistics scheduling, many problems can be reflected as the variants of traveling salesman problem (TSP). In recent years, although there are many achievements and developments on the study of intelligent optimization algorithms for TSP and its variants, the challenge and problem are still existed: while the scale of problem is large, there is the dimension disaster problem; the optimization algorithms for TSP variants are easy to fall into the local optimum. Multiple balanced traveling salesmen problem (MBTSP) is a variant of TSP, it can be used in the fields such as optimizing multiple gas turbine engines. Since MBTSP is NP-hard problem, many intelligent optimization algorithms, such as genetic algorithm and ITO algorithm, have been used to solve it. However, while the scale of MBTSP is large, the traditional algorithms are easy to fall into local optimum. Aiming at the problem, this paper extends the scale of MBTSP to large scale scenes, and proposes a novel ITO algorithm (NITO) based on crossover operator and local search for large scale MBTSP. For NITO algorithm, the drift operator and volatility operator of ITO algorithm are redesigned, which are carried out by improved crossover operator and local search. The extensive experiments show that NITO can demonstrate better solution quality than the compared state-of-the-art algorithms. (C) 2021 Elsevier B.V. All rights reserved.