Novel Ant Colony Optimization Methods for Simplifying Solution Construction in Vehicle Routing Problems

Xinyu Wang, Tsan Ming Choi, Haikuo Liu, Xiaohang Yue

Research output: Journal article publicationJournal articleAcademic researchpeer-review

86 Citations (Scopus)


As a novel evolutionary searching technique, ant colony optimization (ACO) has gained wide research attention and can be used as a tool for optimizing an array of mathematical functions. In transportation systems, when ACO is applied to solve the vehicle routing problem (VRP), the path of each ant is only "part" of a feasible solution. In other words, multiple ants' paths may constitute one feasible solution. Previous works mainly focus on the algorithm itself, such as revising the pheromone updating scheme and combining ACO with other optimization methods. However, this body of literature ignores the important procedure of constructing feasible solutions with those "parts". To overcome this problem, this paper presents a novel ACO algorithm (called AMR) to solve the VRP. The proposed algorithm allows ants to go in and out the depots more than once until they have visited all customers, which simplifies the procedure of constructing feasible solutions. To further enhance AMR, we propose two extensions (AMR-SA and AMR-SA-II) by integrating AMR with other saving algorithms. The computational results for standard benchmark problems are reported and compared with those from other ACO methods. Experimental results indicate that the proposed algorithms outperform the existing ACO algorithms.
Original languageEnglish
Article number7462285
Pages (from-to)3132-3141
Number of pages10
JournalIEEE Transactions on Intelligent Transportation Systems
Issue number11
Publication statusPublished - 1 Nov 2016


  • Ant colony optimization (ACO)
  • feasible solutions
  • paths
  • saving algorithm
  • vehicle routing problem (VRP)

ASJC Scopus subject areas

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications


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