Solving the Probabilistic TSP with Ant Colony Optimization

Branke, Jürgen; Guntsch, Michael

doi:10.1023/b:jmma.0000049380.86987.d4

Cited by 42 publications

(11 citation statements)

References 16 publications

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“…In this study, the proposed GA needs to repeatedly compare two solutions (i.e., the new solution before and after local search procedure, which is described in the next section) based on their values of E [τ ]. Therefore, the depth approximation originally proposed by Branke & Guntsch (2004) was adopted. The depth approximate evaluation of E[τ ] shown in (3) have been used to significantly increase the computation efficiency under the scatter search framework (Liu, 2006).…”

Section: Approximate Evaluation For the A Priori Tourmentioning

confidence: 99%

Solving the Probabilistic Travelling Salesman Problem Based on Genetic Algorithm with Queen Selection Scheme

Liu¹

2008

Traveling Salesman Problem

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Section: Approximate Evaluation For the A Priori Tourmentioning

confidence: 99%

Solving the Probabilistic Travelling Salesman Problem Based on Genetic Algorithm with Queen Selection Scheme

Liu¹

2008

Traveling Salesman Problem

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“…The preliminary results showed that the PTSP-specific approach is more effective than its' TSP counterpart when the instance probability values are less than 0.5. Branke and Guntsch (2004) explored the idea to employ an ad-hoc approximation to replace the exact PTSP objective function, and showed that the computation time can be significantly reduced without major loss in solution quality. Bianchi (2006) and Bianchi and Gambardella (2007) proposed pACS+1-shift, which integrates the PTSP-specific ACS with 1-shift (Bertsimas and Howell, 1993;Bianchi et al, 2005), a local search tailored for the PTSP.…”

Section: Introductionmentioning

confidence: 99%

Estimation-based ant colony optimization and local search for the probabilistic traveling salesman problem

et al. 2009

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The use of ant colony optimization for solving stochastic optimization problems has received a significant amount of attention in recent years. In this paper, we present a study of enhanced ant colony optimization algorithms for tackling a stochastic optimization problem, the probabilistic traveling salesman problem. In particular, we propose an empirical estimation approach to evaluate the cost of the solutions constructed by the ants. Moreover, we use a recent estimation-based iterative improvement algorithm as a local search. Experimental results on a large number of problem instances show that the proposed ant colony optimization algorithms outperform the current best algorithm tailored to solve the given problem, which also happened to be an ant colony optimization algorithm. As a consequence, we have obtained a new state-of-the-art ant colony optimization algorithm for the probabilistic traveling salesman problem.

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“…We note that both Branke and Guntsch [19] and Liu [47] experiment with truncation-based approximate function evaluations within their ant-colony and scatter-search metaheuristics, respectively. Branke and Guntsch [19] consider instances as large as 1379 customers and demonstrate that an approximate function evaluation embedded in ant-colony optimization reduces computation time by almost 30% relative to a full evaluation.…”

Section: Truncation and Approximation Functionmentioning

confidence: 99%

“…Branke and Guntsch [19] consider instances as large as 1379 customers and demonstrate that an approximate function evaluation embedded in ant-colony optimization reduces computation time by almost 30% relative to a full evaluation. Interestingly, they found better speedup when using the approximation with heuristic search methods other than ant colony optimization.…”

Section: Truncation and Approximation Functionmentioning

confidence: 99%

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Challenges and Advances in A Priori Routing

Campbell

Thomas

Operations Research/Computer Science Interfaces

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Summary. An a priori route is a route which specifies an ordering of all possible customers that a particular driver may need to visit. The driver may then skip those customers on the route who do not receive a delivery. Despite the prevalence of a priori routing, construction of these routes still presents considerable challenges. Exact methods are limited to small problem sizes, and even heuristic methods are intractable in the face of real-world-sized instances. In this chapter, we will review some of the ideas that have emerged in recent years to help solve these larger instances. We focus on the probabilistic traveling salesman problem and the recently introduced probabilistic traveling salesman problem with deadlines and discuss how objective-function approximations can reduce computation time without significantly impacting solution quality. We will also present several open research questions in a priori routing.

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Solving the Probabilistic TSP with Ant Colony Optimization

Cited by 42 publications

References 16 publications

Solving the Probabilistic Travelling Salesman Problem Based on Genetic Algorithm with Queen Selection Scheme

Solving the Probabilistic Travelling Salesman Problem Based on Genetic Algorithm with Queen Selection Scheme

Estimation-based ant colony optimization and local search for the probabilistic traveling salesman problem

Challenges and Advances in A Priori Routing

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