Abstract: A mathematical model (LCTSP) of the low-carbon traveling salesman problem is established and its validity is verified. A discrete particle swarm optimization algorithm based on heuristic information is proposed. Firstly, according to the distance and load information, a novel discrete individual generation operator is designed, which adopts the multi-mutation strategy for the individual itself to maintain the “inertia” of the individual, and adopts the greedy crossover strategy to realize the information interaction between the personal best and the global best. Secondly, the personal best is searched locally based on the priority unloading information, and the population tracking object is adjusted to jump out of the local optimum quickly. Thirdly, according to the degree of population assimilation, the point insertion method and the 2-Opt operator are used to search the global best in a refined way, in order to enhance the mining ability, improve the search accuracy and reduce the rate of population assimilation. Experimental results in a group of low-carbon traveling salesman problems with different scales show that the proposed algorithm has higher accuracy than six state-of-the-art algorithms.

Key words: low-carbon traveling salesman problem, carbon emission, particle swarm optimization, heuristic information