Dual-information guided ant colony optimization algorithm for green multi-compartment vehicle routing problem
GUO Ning
SHEN Qiu-yi
QIAN Bin
NA Jing
HU Rong
MAO Jian-lin
Abstract：For dealing with the green multi-compartment vehicle routing problem(GMCVRP)widely existing in actual transportation,a dual-information guided ant colony optimization algorithm(DIACO)is proposed to solve it.First,in the global search stage of DIACO,the pheromone concentration matrix(PCM)in the traditional ant colony optimization algo-rithm(TACO)is reconstructed.The reconstructed PCM contains both customer block information and customer sequence information.That is,the dual-information PCM(DIPCM)is established so as to more comprehensively learn and accumu-late high-quality solution information.Three types of heuristic methods are adopted to generate higher quality individuals for initializing DIPCM,which can guide the algorithm to search for high-quality regions in the solution space quickly.Sec-ond,in the local search stage of DIACO,multiple variable neighborhood operations combined with adaptive strategy are designed to perform in-depth searches on high-quality regions of the solution space.Third,the pheromone concentration balance mechanism is proposed to prevent the search from stagnating.Last,simulation tests and algorithm comparisons are carried out with different scale examples.The results show that DIACO is an effective algorithm for solving the GMCVRP.
Keywords：multi-compartment vehicle routing problemgreenant colony optimization algorithmdual-information guidedpheromone concentration balance mechanism
Publication Date：2024-06-28
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：12( 1067-1078 )