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Title: Novel ant colony optimization methods for simplifying solution construction in vehicle routing problems
Authors: Wang, X
Choi, TM 
Liu, H
Yue, X
Keywords: Ant colony optimization (ACO)
Feasible solutions
Saving algorithm
Vehicle routing problem (VRP)
Issue Date: 2016
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on intelligent transportation systems, 2016, v. 17, no. 11, 7462285, p. 3132-3141 How to cite?
Journal: IEEE transactions on intelligent transportation systems 
Abstract: 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.
ISSN: 1524-9050
EISSN: 1558-0016
DOI: 10.1109/TITS.2016.2542264
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