Modified ant colony optimization with improved tour construction and pheromone updating strategies for traveling salesma
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METHODOLOGIES AND APPLICATION
Modified ant colony optimization with improved tour construction and pheromone updating strategies for traveling salesman problem Wei Gao1
Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract To improve ant colony optimization (ACO) for traveling salesman problem (TSP), its two main strategies which are tour construction and pheromone updating have been modified, and one modified ACO (MACO) has been proposed. For the first strategy (tour construction strategy), one new method to construct tours by combining paths of two meeting ants has been applied. And for second strategy (pheromone updating strategy), one new method to polarize pheromone density of all paths has been proposed. Based on the applications for 40 standard benchmark TSP instances (datasets) ranging from 29 to 13,509 cities, the good performance of the MACO is verified. To verify the MACO deeply, based on the applications for some standard TSP instances, the computing results of MACO are compared with three typical state-of-the-art algorithms based on ACO methods. Moreover, based on the applications for some standard TSP instances, the computing results of MACO are compared with 10 state-of-the-art metaheuristic algorithms. The comparison studies show that the MACO can attain the optimal solution with higher accuracy, no matter how complicated the TSPs are. And its performance is the best, comparing to all state-of-the-art algorithms. At last, two new strategies used in MACO have been analyzed comprehensively by the applications for 25 TSPs. The results show that the first strategy is mainly used to improve the computing efficiency, and the second one is mainly used to improve the computing effect. Keywords TSP MACO Tour construction strategy Pheromone updating strategy Comparison study
1 Introduction Ant colony optimization (ACO) (Dorigo and Stu¨tzle 2004) is a swarm intelligent algorithm inspired by the foraging behavior of real ants. The first algorithm of ACO is called ant system (Dorigo et al. 1991) proposed by three Italy scholars (Dorigo, Colorni and Maniezzo) in 1991. As the original version of various ant colony algorithms, the ant system has some obvious merits including distributed computation, strong robustness and application of constructive greedy heuristic. Therefore, it has become a hot topic from its creating and has been used to solve difficult combinatorial optimization problems, such as traveling
Communicated by V. Loia. & Wei Gao [email protected] 1
College of Civil and Transportation Engineering, Hohai University, 1 Xikang Road, Nanjing 210024, People’s Republic of China
salesman problem (TSP) (Dorigo and Gambardella 1997; Dorigo et al. 1996), vehicle routing problems (Reimann et al. 2004), packet-switched communication network problems (Di Caro and Dorigo 1998; Schoonderwoerd et al. 1997), the network design problem (Poorzahedy and Abulghasemi 2005), land cover zoning and planning (Li et al. 2011), and others. However, as one
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