Dynamic Programming Methodologies in Very Large Scale Neighborhood Search Applied to the Traveling Salesman Problem

The whale optimization algorithm is a new type of swarm intelligence bionic optimization algorithm, which has achieved good optimization results in solving continuous optimization problems. However, it has less application in discrete optimization problems. A variable neighborhood discrete whale optimization algorithm for the traveling salesman problem (TSP) is studied in this paper. The discrete code is designed first, and then the adaptive weight, Gaussian disturbance, and variable neighborhood search strategy are introduced, so that the population diversity and the global search ability of the algorithm are improved. The proposed algorithm is tested by 12 classic problems of the Traveling Salesman Problem Library (TSPLIB). Experiment results show that the proposed algorithm has better optimization performance and higher efficiency compared with other popular algorithms and relevant literature.

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Unconventional GVNS for Solving the Garbage Collection Problem with Time Windows

Technologies ◽

10.3390/technologies7030061 ◽

2019 ◽

Vol 7 (3) ◽

pp. 61 ◽

Cited By ~ 2

Author(s):

Christos Papalitsas ◽

Theodore Andronikos

Keyword(s):

Traveling Salesman Problem ◽

Optimization Problems ◽

Time Windows ◽

Experimental Information ◽

Traveling Salesman ◽

Neighborhood Search ◽

Np Hard ◽

The Novel ◽

Global Positioning ◽

The Traveling Salesman Problem

GVNS, which stands for General Variable Neighborhood Search, is an established and commonly used metaheuristic for the expeditious solution of optimization problems that belong to the NP-hard class. This paper introduces an expansion of the standard GVNS that borrows principles from quantum computing during the shaking stage. The Traveling Salesman Problem with Time Windows (TSP-TW) is a characteristic NP-hard variation in the standard Traveling Salesman Problem. One can utilize TSP-TW as the basis of Global Positioning System (GPS) modeling and routing. The focus of this work is the study of the possible advantages that the proposed unconventional GVNS may offer to the case of garbage collector trucks GPS. We provide an in-depth presentation of our method accompanied with comprehensive experimental results. The experimental information gathered on a multitude of TSP-TW cases, which are contained in a series of tables, enable us to deduce that the novel GVNS approached introduced here can serve as an effective solution for this sort of geographical problems.

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Exact And Heuristic Procedures For The Traveling Salesman Problem With Precedence Constraints, Based On Dynamic Programming

INFOR Information Systems and Operational Research ◽

10.1080/03155986.1994.11732235 ◽

1994 ◽

Vol 32 (1) ◽

pp. 19-32 ◽

Cited By ~ 5

Author(s):

Lucio Bianco ◽

Aristide Mingozzi ◽

Salvatore Ricciardelli ◽

Massimo Spadoni

Keyword(s):

Dynamic Programming ◽

Traveling Salesman Problem ◽

Precedence Constraints ◽

Traveling Salesman ◽

Heuristic Procedures ◽

The Traveling Salesman Problem

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Computational study of neighborhood operator performance on the Traveling Salesman Problem with Time Windows in neighborhood search based frameworks (RTS, VNS)

3rd IEEE International Symposium on Logistics and Industrial Informatics ◽

10.1109/lindi.2011.6031160 ◽

2011 ◽

Cited By ~ 2

Author(s):

Michael Bogl ◽

Gunther Zapfel ◽

Michael Affenzeller

Keyword(s):

Traveling Salesman Problem ◽

Computational Study ◽

Time Windows ◽

Traveling Salesman ◽

Neighborhood Search ◽

Operator Performance ◽

The Traveling Salesman Problem

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Local Clustering Organization (LCO) Solving a Large-Scale TSP

Journal of Robotics and Mechatronics ◽

10.20965/jrm.2005.p0560 ◽

2005 ◽

Vol 17 (5) ◽

pp. 560-567 ◽

Cited By ~ 3

Author(s):

Masashi Furukawa ◽

◽

Michiko Watanabe ◽

Yusuke Matsumura ◽

◽

...

Keyword(s):

Traveling Salesman Problem ◽

Numerical Experiments ◽

Large Scale ◽

The Self ◽

Traveling Salesman ◽

Self Organizing Map ◽

Two Cities ◽

Local Clustering ◽

Different Types ◽

The Traveling Salesman Problem

The traveling salesman problem (TSP) is one of the most difficult problems that occur in different types of industrial scheduling situations. We propose a solution, involving local clustering organization (LCO), for a large-scale TSP based on the principle of the self-organizing map (SOM). Although the SOM can solve TSPs, it is not applicable to practical TSPs because the SOM references city coordinates and assigns synapses to coordinates. LCO indirectly uses the SOM principle and, instead of city coordinates, references costs between two cities, to determine the sequence of cities. We apply LCO to a large-scale TSP to determine its efficiency in numerical experiments. Results demonstrate that LCO obtains the desired solutions.

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