Solving the traveling salesman problem based on an adaptive simulated annealing algorithm with greedy search

Geng, Xin; Chen, Zhihua; Yang, Wei; Shi, Deqian; Zhao, Kai

doi:10.1016/j.asoc.2011.01.039

Cited by 208 publications

(114 citation statements)

References 34 publications

Supporting

Mentioning

111

Contrasting

Unclassified

Order By: Relevance

“…For the solution of TSP, the authors [15] improved adaptive SA with greedy search and they introduced three different mutation strategies for the generation of new solutions. Thus the convergence of SA is improved compared to several other algorithms in the literature.…”

Section: Literature Reviewmentioning

confidence: 99%

A New Simulated Annealing Approach for Travelling Salesman Problem

Bayram

Şahin

2013

MCA

View full text Add to dashboard Cite

Abstract-The aim of this study is to improve searching capability of simulated annealing (SA) heuristic through integration of two new neighborhood mechanisms. Due to its ease of formulation, difficulty to solve and various real life applications several Travelling Salesman Problems (TSP) were selected from the literature for the testing of the proposed methods. The proposed methods were also compared to conventional SA with swap neighborhood. The results have shown that the proposed techniques are more effective than conventional SA, both in terms of solution quality and time.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

A New Simulated Annealing Approach for Travelling Salesman Problem

Bayram

Şahin

2013

MCA

View full text Add to dashboard Cite

show abstract

“…To this end, we have selected two algorithms that have been vastly used in literature Simulated Annealing [27] and Tabu Search [28]. Both algorithms have been used to solve graphbased optimization problems [29] [30] with similar characteristics to the one presented here. These two alternative algorithmic approaches have been widely applied in several domains as it is discussed in [31].…”

Section: Experimental Comparisonmentioning

confidence: 99%

Learning semantically-annotated routes for context-aware recommendations on map navigation systems

Mocholi

Jaén

Krynicki

et al. 2012

Applied Soft Computing

View full text Add to dashboard Cite

ElsevierMocholi Agües, JA.; Jaén Martínez, FJ.; Krynicki, KK.; Catalá Bolós, A.;Picón, A.;Cadenas, A. (2012) ABSTRACTModern technology has brought many changes to our everyday lives. Our need to be in constant touch with others has been met with the cellphone, which has become our companion and the convergence point of many technological advances. The combination of capabilities such as browsing the Internet and GPS reception has multiplied the services and applications based on the current location of the user.However, providing the user with these services has certain drawbacks. Although map navigation systems are the most meaningful way of displaying this information, the user still has to manually set up the filter in order to obtain a non-bloated visualization of the map and the available services. To tackle this problem, we present here a semantic multicriteria ant colony algorithm capable of learning the user's routes, including associated context information, and then predicting the most likely route a user is following, given his current location and context data. This knowledge could then be used as the basis for offering services related to his current (or most likely future) context data close to the path he is following. Our experimental results show that our algorithm is capable of obtaining consistent solutions sets even when multiple objective ontological terms are included in the process.

show abstract

“…SA parameters are: T int = 1000 and T end = 0.0025 (Geng et al, 2011), T cool = 0.97 and the number of iteration = 20000 (Seshadri, 2006).…”

Section: Simulated Annealing Algorithm (Sa)mentioning

confidence: 99%

“…Moreover, many randomized approaches are shown to perform well on the TSP, e.g., the ant colony optimization (Dorigo and Gambardella, 1997), the tabu search (Gendreau et al, 1998), the genetic algorithm (Chatterjee et al, 1996;Moon et al, 2002), the cross entropy method (Boer et al, 2005), the particle swarm optimization (Shi et al, 2007) and the simulated annealing algorithm (Geng et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

A Gaussian Process Regression Model for the Traveling Salesman Problem

Kongkaew

Pichitlamken

2012

Journal of Computer Science

View full text Add to dashboard Cite

Problem statement: Traveling Salesman Problem (TSP) is a famous NP hard problem. Many approaches have been proposed up to date for solving TSP. We consider a TSP tour as a dependent variable and its corresponding distance as an independent variable. If a predictive function can be formulated from arbitrary sample tours, the optimal tour may be predicted from this function. Approach: In this study, a combined procedure of the Nearest Neighbor (NN) method, Gaussian Process Regression (GPR) and the iterated local search is proposed to solve a deterministic symmetric TSP with a single salesman. The first tour in the sample is constructed by the nearest neighbor algorithm and it is used to construct other tours by the random 2-exchange swap. These tours and their total distances are training data for a Gaussian process regression model. A GPR solution is further improved with the iterated 2-opt method. In the numerical experiments, our algorithm is tested on many TSP instances and it is compared with the Genetic Algorithm (GA) and the Simulated Annealing (SA) algorithm. Results: The proposed method can find good TSP tours within a reasonable computational time for a wide range of TSP test problems. In some cases, it outperforms GA and SA. Conclusion: Our proposed algorithm is promising for solving the TSP.

show abstract

Solving the traveling salesman problem based on an adaptive simulated annealing algorithm with greedy search

Cited by 208 publications

References 34 publications

A New Simulated Annealing Approach for Travelling Salesman Problem

A New Simulated Annealing Approach for Travelling Salesman Problem

Learning semantically-annotated routes for context-aware recommendations on map navigation systems

A Gaussian Process Regression Model for the Traveling Salesman Problem

Contact Info

Product

Resources

About