Relevant bibliographies by topics / Traveling-salesman problem. Genetic algorithms. Heuristic programming

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters

Academic literature on the topic 'Traveling-salesman problem. Genetic algorithms. Heuristic programming'

Author: Grafiati
Published: 4 June 2021
Last updated: 9 February 2022

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Traveling-salesman problem. Genetic algorithms. Heuristic programming.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Traveling-salesman problem. Genetic algorithms. Heuristic programming"

1

Zhou, Ai-Hua, Li-Peng Zhu, Bin Hu, et al. "Traveling-Salesman-Problem Algorithm Based on Simulated Annealing and Gene-Expression Programming." Information 10, no. 1 (2018): 7. http://dx.doi.org/10.3390/info10010007.

Full text
Abstract:
The traveling-salesman problem can be regarded as an NP-hard problem. To better solve the best solution, many heuristic algorithms, such as simulated annealing, ant-colony optimization, tabu search, and genetic algorithm, were used. However, these algorithms either are easy to fall into local optimization or have low or poor convergence performance. This paper proposes a new algorithm based on simulated annealing and gene-expression programming to better solve the problem. In the algorithm, we use simulated annealing to increase the diversity of the Gene Expression Programming (GEP) population and improve the ability of global search. The comparative experiments results, using six benchmark instances, show that the proposed algorithm outperforms other well-known heuristic algorithms in terms of the best solution, the worst solution, the running time of the algorithm, the rate of difference between the best solution and the known optimal solution, and the convergent speed of algorithms.
APA, Harvard, Vancouver, ISO, and other styles
2

Abdul-Niby, M., M. Alameen, A. Salhieh, and A. Radhi. "Improved Genetic and Simulating Annealing Algorithms to Solve the Traveling Salesman Problem Using Constraint Programming." Engineering, Technology & Applied Science Research 6, no. 2 (2016): 927–30. http://dx.doi.org/10.48084/etasr.627.

Full text
Abstract:
The Traveling Salesman Problem (TSP) is an integer programming problem that falls into the category of NP-Hard problems. As the problem become larger, there is no guarantee that optimal tours will be found within reasonable computation time. Heuristics techniques, like genetic algorithm and simulating annealing, can solve TSP instances with different levels of accuracy. Choosing which algorithm to use in order to get a best solution is still considered as a hard choice. This paper suggests domain reduction as a tool to be combined with any meta-heuristic so that the obtained results will be almost the same. The hybrid approach of combining domain reduction with any meta-heuristic encountered the challenge of choosing an algorithm that matches the TSP instance in order to get the best results.
APA, Harvard, Vancouver, ISO, and other styles
3

da Silva, Felipe Augusto Moreira, Antonio Carlos Moretti, and Anibal Tavares de Azevedo. "A Scheduling Problem in the Baking Industry." Journal of Applied Mathematics 2014 (2014): 1–14. http://dx.doi.org/10.1155/2014/964120.

Full text
Abstract:
This paper addresses a scheduling problem in an actual industrial environment of a baking industry where production rates have been growing every year and the need for optimized planning becomes increasingly important in order to address all the features presented by the problem. This problem contains relevant aspects of production, such as parallel production, setup time, batch production, and delivery date. We will also consider several aspects pertaining to transportation, such as the transportation capacity with different vehicles and sales production with several customers. This approach studies an atypical problem compared to those that have already been studied in literature. In order to solve the problem, we suggest two approaches: using the greedy heuristic and the genetic algorithm, which will be compared to small problems with the optimal solution solved as an integer linear programming problem, and we will present results for a real example compared with its upper bounds. The work provides us with a new mathematical formulation of scheduling problem that is not based on traveling salesman problem. It considers delivery date and the profit maximization and not the makespan minimization. And it also provides an analysis of the algorithms runtime.
APA, Harvard, Vancouver, ISO, and other styles
4

Ahmadi, Bilal, and Devi Jayawati. "RANCANG BANGUN DECISION SUPPORT SYSTEM UNTUK PEMILIHAN RUTE PENGIRIMAN PAKET PADA PERUSAHAAN PENYEDIA JASA LOGISTIK." Jurnal Manajemen Industri dan Logistik 1, no. 2 (2018): 101–10. http://dx.doi.org/10.30988/jmil.v1i2.10.

Full text
Abstract:
Traveling Salesman Problem (TSP) is a well-known route optimization problem in which a set of customers is visited only once and all demands are satisfied within single route. Package delivery by a courier company can be considered into this type of problem. While many of the previous research had been conducted, the application of the solutions were limited due to unreal distance measure. This paper developed a decision support system for route optimization using free online maps from Google and Google Maps Application Programming Interface (API). Genetic Algorithm was employed to generate solutions within relatively short time. Furthermore, the solutions was viewed as a function of distance and time. There were some limitations from Google in case of acquiring distance data with the free of charge mechanism. Future research can tackle this backdraw by putting more customer points. Other heuristic methods could also be used for comparison purpose
APA, Harvard, Vancouver, ISO, and other styles
5

Jafarzadeh, H., N. Moradinasab, and M. Elyasi. "An Enhanced Genetic Algorithm for the Generalized Traveling Salesman Problem." Engineering, Technology & Applied Science Research 7, no. 6 (2017): 2260–65. http://dx.doi.org/10.48084/etasr.1570.

Full text
Abstract:
The generalized traveling salesman problem (GTSP) deals with finding the minimum-cost tour in a clustered set of cities. In this problem, the traveler is interested in finding the best path that goes through all clusters. As this problem is NP-hard, implementing a metaheuristic algorithm to solve the large scale problems is inevitable. The performance of these algorithms can be intensively promoted by other heuristic algorithms. In this study, a search method is developed that improves the quality of the solutions and competition time considerably in comparison with Genetic Algorithm. In the proposed algorithm, the genetic algorithms with the Nearest Neighbor Search (NNS) are combined and a heuristic mutation operator is applied. According to the experimental results on a set of standard test problems with symmetric distances, the proposed algorithm finds the best solutions in most cases with the least computational time. The proposed algorithm is highly competitive with the published until now algorithms in both solution quality and running time.
APA, Harvard, Vancouver, ISO, and other styles
6

Saharidis, Georgios K. D. "Review of Solution Approaches for the Symmetric Traveling Salesman Problem." International Journal of Information Systems and Supply Chain Management 7, no. 1 (2014): 73–87. http://dx.doi.org/10.4018/ijisscm.2014010105.

Full text
Abstract:
In this paper, the main known exact and heuristic solution approaches and algorithms for the symmetric Traveling Salesman Problem (TSP), published after 1992, are surveyed. The paper categorize the most important existing algorithm to 6 main groups: i) Genetic algorithms, ii) Ant colony methods, iii) Neural Methods, iv) Local search algorithms and Tabu search, v) Lagrangian methods and vi) Branch and bound and branch & cut algorithms.
APA, Harvard, Vancouver, ISO, and other styles
7

Oltean, Mihai. "Evolving Evolutionary Algorithms Using Linear Genetic Programming." Evolutionary Computation 13, no. 3 (2005): 387–410. http://dx.doi.org/10.1162/1063656054794815.

Full text
Abstract:
A new model for evolving Evolutionary Algorithms is proposed in this paper. The model is based on the Linear Genetic Programming (LGP) technique. Every LGP chromosome encodes an EA which is used for solving a particular problem. Several Evolutionary Algorithms for function optimization, the Traveling Salesman Problem and the Quadratic Assignment Problem are evolved by using the considered model. Numerical experiments show that the evolved Evolutionary Algorithms perform similarly and sometimes even better than standard approaches for several well-known benchmarking problems.
APA, Harvard, Vancouver, ISO, and other styles
8

El Hassani, Hicham, Said Benkachcha, and Jamal Benhra. "New Genetic Operator (Jump Crossover) for the Traveling Salesman Problem." International Journal of Applied Metaheuristic Computing 6, no. 2 (2015): 33–44. http://dx.doi.org/10.4018/ijamc.2015040103.

Full text
Abstract:
Inspired by nature, genetic algorithms (GA) are among the greatest meta-heuristics optimization methods that have proved their effectiveness to conventional NP-hard problems, especially the traveling salesman problem (TSP) which is one of the most studied supply chain management problems. This paper proposes a new crossover operator called Jump Crossover (JMPX) for solving the travelling salesmen problem using a genetic algorithm (GA) for near-optimal solutions, to conclude on its efficiency compared to solutions quality given by other conventional operators to the same problem, namely, Partially matched crossover (PMX), Edge recombination Crossover (ERX) and r-opt heuristic with consideration of computational overload. The authors adopt a low mutation rate to isolate the search space exploration ability of each crossover. The experimental results show that in most cases JMPX can remarkably improve the solution quality of the GA compared to the two existing classic crossover approaches and the r-opt heuristic.
APA, Harvard, Vancouver, ISO, and other styles
9

Pál, Károly F. "Genetic algorithms for the traveling salesman problem based on a heuristic crossover operation." Biological Cybernetics 69, no. 5-6 (1993): 539–46. http://dx.doi.org/10.1007/bf01185425.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Hamdan, Basma, Hamdi Bashir, and Ali Cheaitou. "A novel clustering method for breaking down the symmetric multiple traveling salesman problem." Journal of Industrial Engineering and Management 14, no. 2 (2021): 199. http://dx.doi.org/10.3926/jiem.3287.

Full text
Abstract:
Purpose: This study proposes a new two-stage clustering method to break down the symmetric multiple traveling salesman problem (mTSP) into several single standard traveling salesman problems, each of which can then be solved separately using a heuristic optimization algorithm.Design/methodology/approach: In the initial stage, a modified form of factor analysis is used to identify clusters of cities. In the second stage, the cities are allocated to the identified clusters using an integer-programming model. A comparison with the k-means++ clustering algorithm, one of the most popular clustering algorithms, was made to evaluate the performance of the proposed method in terms of four objective criteria.Findings: Computational results and comparison on 63 problems revealed that the proposed method is promising for producing quality clusters and thus for enhancing the performance of heuristic optimization algorithms in solving the mTSP.Originality/value: Unlike previous studies, this study tackles the issue of improving the performance of clustering-based optimization approaches in solving the mTSP by proposing a new clustering method that produces better cluster solutions rather than by proposing a new or improved version of a heuristic optimization algorithm for finding optimal routes.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Traveling-salesman problem. Genetic algorithms. Heuristic programming"

1

Skidmore, Gerald. "Metaheuristics and combinatorial optimization problems /." Online version of thesis, 2006. https://ritdml.rit.edu/dspace/handle/1850/2319.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Bianchi, Leonora. "Ant colony optimization and local search for the probabilistic traveling salesman problem: a case study in stochastic combinatorial optimization." Doctoral thesis, Universite Libre de Bruxelles, 2006. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/210877.

Full text
Abstract:
In this thesis we focus on Stochastic combinatorial Optimization Problems (SCOPs), a wide class of combinatorial optimization problems under uncertainty, where part of the information about the problem data is unknown at the planning stage, but some knowledge about its probability distribution is assumed.<p><p>Optimization problems under uncertainty are complex and difficult, and often classical algorithmic approaches based on mathematical and dynamic programming are able to solve only very small problem instances. For this reason, in recent years metaheuristic algorithms such as Ant Colony Optimization, Evolutionary Computation, Simulated Annealing, Tabu Search and others, are emerging as successful alternatives to classical approaches.<p><p>In this thesis, metaheuristics that have been applied so far to SCOPs are introduced and the related literature is thoroughly reviewed. In particular, two properties of metaheuristics emerge from the survey: they are a valid alternative to exact classical methods for addressing real-sized SCOPs, and they are flexible, since they can be quite easily adapted to solve different SCOPs formulations, both static and dynamic. On the base of the current literature, we identify the following as the key open issues in solving SCOPs via metaheuristics: <p>(1) the design and integration of ad hoc, fast and effective objective function approximations inside the optimization algorithm;<p>(2) the estimation of the objective function by sampling when no closed-form expression for the objective function is available, and the study of methods to reduce the time complexity and noise inherent to this type of estimation;<p>(3) the characterization of the efficiency of metaheuristic variants with respect to different levels of stochasticity in the problem instances. <p><p>We investigate the above issues by focusing in particular on a SCOP belonging to the class of vehicle routing problems: the Probabilistic Traveling Salesman Problem (PTSP). For the PTSP, we consider the Ant Colony Optimization metaheuristic and we design efficient local search algorithms that can enhance its performance. We obtain state-of-the-art algorithms, but we show that they are effective only for instances above a certain level of stochasticity, otherwise it is more convenient to solve the problem as if it were deterministic.<p>The algorithmic variants based on an estimation of the objective function by sampling obtain worse results, but qualitatively have the same behavior of the algorithms based on the exact objective function, with respect to the level of stochasticity. Moreover, we show that the performance of algorithmic variants based on ad hoc approximations is strongly correlated with the absolute error of the approximation, and that the effect on local search of ad hoc approximations can be very degrading.<p><p>Finally, we briefly address another SCOP belonging to the class of vehicle routing problems: the Vehicle Routing Problem with Stochastic Demands (VRPSD). For this problem, we have implemented and tested several metaheuristics, and we have studied the impact of integrating in them different ad hoc approximations.<p><br>Doctorat en sciences appliquées<br>info:eu-repo/semantics/nonPublished
APA, Harvard, Vancouver, ISO, and other styles
3

Meuth, Ryan James. "Meta-learning computational intelligence architectures." Diss., Rolla, Mo. : Missouri University of Science and Technology, 2009. http://scholarsmine.mst.edu/thesis/pdf/Meuth_09007dcc80722172.pdf.

Full text
Abstract:
Thesis (Ph. D.)--Missouri University of Science and Technology, 2009.<br>Vita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed January 5, 2010) Includes bibliographical references (p. 152-159).
APA, Harvard, Vancouver, ISO, and other styles

Books on the topic "Traveling-salesman problem. Genetic algorithms. Heuristic programming"

1

The traveling salesman: Computational solutions for TSP applications. Springer-Verlag, 1994.

Find full text
APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "Traveling-salesman problem. Genetic algorithms. Heuristic programming"

1

Hrbek, Václav, and Jan Merta. "Searching the Hyper-heuristic for the Traveling Salesman Problem with Time Windows by Genetic Programming." In Software Engineering Perspectives in Intelligent Systems. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63322-6_81.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Paul, Victer, Ganeshkumar C, and Jayakumar L. "Performance Evaluation of Population Seeding Techniques of Permutation-Coded GA Traveling Salesman Problems Based Assessment." In Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-8048-6.ch053.

Full text
Abstract:
Genetic algorithms (GAs) are a population-based meta-heuristic global optimization technique for dealing with complex problems with a very large search space. The population initialization is a crucial task in GAs because it plays a vital role in the convergence speed, problem search space exploration, and also the quality of the final optimal solution. Though the importance of deciding problem-specific population initialization in GA is widely recognized, it is hardly addressed in the literature. In this article, different population seeding techniques for permutation-coded genetic algorithms such as random, nearest neighbor (NN), gene bank (GB), sorted population (SP), and selective initialization (SI), along with three newly proposed ordered-distance-vector-based initialization techniques have been extensively studied. The ability of each population seeding technique has been examined in terms of a set of performance criteria, such as computation time, convergence rate, error rate, average convergence, convergence diversity, nearest-neighbor ratio, average distinct solutions and distribution of individuals. One of the famous combinatorial hard problems of the traveling salesman problem (TSP) is being chosen as the testbed and the experiments are performed on large-sized benchmark TSP instances obtained from standard TSPLIB. The scope of the experiments in this article is limited to the initialization phase of the GA and this restricted scope helps to assess the performance of the population seeding techniques in their intended phase alone. The experimentation analyses are carried out using statistical tools to claim the unique performance characteristic of each population seeding techniques and best performing techniques are identified based on the assessment criteria defined and the nature of the application.
APA, Harvard, Vancouver, ISO, and other styles
3

Sahin, Yusuf, Erdal Aydemir, Kenan Karagul, Sezai Tokat, and Burhan Oran. "Metaheuristics Approaches for the Travelling Salesman Problem on a Spherical Surface." In Interdisciplinary Perspectives on Operations Management and Service Evaluation. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-5442-5.ch005.

Full text
Abstract:
Traveling salesman problem in which all the vertices are assumed to be on a spherical surface is a special case of the conventional travelling salesman problem. There are exact and approximate algorithms for the travelling salesman problem. As the solution time is a performance parameter in most real-time applications, approximate algorithms always have an important area of research for both researchers and engineers. In this chapter, approximate algorithms based on heuristic methods are considered for the travelling salesman problem on the sphere. Firstly, 28 test instances were newly generated on the unit sphere. Then, using various heuristic methods such as genetic algorithms, ant colony optimization, and fluid genetic algorithms, the initial solutions for solving test instances of the traveling salesman problem are obtained in Matlab®. Then, the initial heuristic solutions are used as input for the 2-opt algorithm. The performances and time complexities of the applied methods are analyzed as a conclusion.
APA, Harvard, Vancouver, ISO, and other styles
4

Ganesh, K., R. Dhanlakshmi, A. Tangavelu, and P. Parthiban. "Hybrid Artificial Intelligence Heuristics and Clustering Algorithm for Combinatorial Asymmetric Traveling Salesman Problem." In Utilizing Information Technology Systems Across Disciplines. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-60566-616-7.ch001.

Full text
Abstract:
Problems of combinatorial optimization are characterized by their well-structured problem definition as well as by their huge number of action alternatives in practical application areas of reasonable size. Especially in areas like routing, task allocation, or scheduling, such kinds of problems often occur. Artificial Intelligence Heuristics, otherwise called Meta-heuristic techniques that mimic natural processes, can produce ‘good’ results in reasonable short runs for this class of optimization problems. Even though those bionic heuristics are much more flexible regarding modifications in the problem description when being compared to classical problem specific heuristics, they are often superior in their results. Those bionic heuristics have been developed following the principles of natural processes. In that sense, Genetic Algorithms (GAs) try to imitate the biological evolution of a species in order to achieve an almost optimal state whereas Simulated Annealing (SA) was initially inspired by the laws of thermodynamics in order to cool down a certain matter to its lowest energetic state. This paper develops a set of metaheuristics (GA, SA and Hybrid GA-SA) to solve a variant of combinatorial optimization problem called Asymmetric Traveling Salesman Problem. The set of met heuristics is compared with clustering based heuristic and the results are encouraging.
APA, Harvard, Vancouver, ISO, and other styles
5

El Hassani, Hicham, Said Benkachcha, and Jamal Benhra. "Optimized Crossover JumpX in Genetic Algorithm for General Routing Problems." In Advancements in Applied Metaheuristic Computing. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-4151-6.ch009.

Full text
Abstract:
Inspired by nature, genetic algorithms (GA) are among the greatest meta-heuristics optimization methods that have proved their effectiveness to conventional NP-hard problems, especially the traveling salesman problem (TSP) which is one of the most studied Supply chain management problems. This paper proposes a new crossover operator called Jump Crossover (JMPX) for solving the travelling salesmen problem using a genetic algorithm (GA) for near-optimal solutions, to conclude on its efficiency compared to solutions quality given by other conventional operators to the same problem, namely, Partially matched crossover (PMX), Edge recombination Crossover (ERX) and r-opt heuristic with consideration of computational overload. We adopt the path representation technique for our chromosome which is the most direct representation and a low mutation rate to isolate the search space exploration ability of each crossover. The experimental results show that in most cases JMPX can remarkably improve the solution quality of the GA compared to the two existing classic crossover approaches and the r-opt heuristic.
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Traveling-salesman problem. Genetic algorithms. Heuristic programming' for particular source types:
Journal articles
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography