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Search Results (3)

Search Parameters:
Keywords = close enough traveling salesman problem

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14 pages, 1106 KiB  
Article
A Biogeography-Based Optimization with a Greedy Randomized Adaptive Search Procedure and the 2-Opt Algorithm for the Traveling Salesman Problem
by Cheng-Hsiung Tsai, Yu-Da Lin, Cheng-Hong Yang, Chien-Kun Wang, Li-Chun Chiang and Po-Jui Chiang
Sustainability 2023, 15(6), 5111; https://doi.org/10.3390/su15065111 - 14 Mar 2023
Cited by 20 | Viewed by 3026
Abstract
We develop a novel method to improve biogeography-based optimization (BBO) for solving the traveling salesman problem (TSP). The improved method is comprised of a greedy randomized adaptive search procedure, the 2-opt algorithm, and G2BBO. The G2BBO formulation is derived and the process flowchart [...] Read more.
We develop a novel method to improve biogeography-based optimization (BBO) for solving the traveling salesman problem (TSP). The improved method is comprised of a greedy randomized adaptive search procedure, the 2-opt algorithm, and G2BBO. The G2BBO formulation is derived and the process flowchart is shown in this article. For solving TSP, G2BBO effectively avoids the local minimum problem and accelerates convergence by optimizing the initial values. To demonstrate, we adopt three public datasets (eil51, eil76, and kroa100) from TSPLIB and compare them with various well-known algorithms. The results of G2BBO as well as the other algorithms perform close enough to the optimal solutions in eil51 and eil76 where simple TSP coordinates are considered. In the case of kroa100, with more complicated coordinates, G2BBO shows greater performance over other methods. Full article
(This article belongs to the Special Issue Application of Green Energy Technology in Sustainable Environment)
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16 pages, 2958 KiB  
Article
Evolutionary Algorithm with Geometrical Heuristics for Solving the Close Enough Traveling Salesman Problem: Application to the Trajectory Planning of an Unmanned Aerial Vehicle
by Christophe Cariou, Laure Moiroux-Arvis, François Pinet and Jean-Pierre Chanet
Algorithms 2023, 16(1), 44; https://doi.org/10.3390/a16010044 - 9 Jan 2023
Cited by 8 | Viewed by 2501
Abstract
Evolutionary algorithms have been widely studied in the literature to find sub-optimal solutions to complex problems as the Traveling Salesman Problem (TSP). In such a problem, the target positions are usually static and punctually defined. The objective is to minimize a cost function [...] Read more.
Evolutionary algorithms have been widely studied in the literature to find sub-optimal solutions to complex problems as the Traveling Salesman Problem (TSP). In such a problem, the target positions are usually static and punctually defined. The objective is to minimize a cost function as the minimal distance, time or energy. However, in some applications, as the one addressed in this paper—namely the data collection of buried sensor nodes by means of an Unmanned Aerial Vehicle— the targets are areas with varying sizes: they are defined with respect to the radio communication range of each node, ranging from a few meters to several hundred meters according to various parameters (e.g., soil moisture, burial depth, transmit power). The Unmanned Aerial Vehicle has to enter successively in these dynamic areas to collect the data, without the need to pass at the vertical of each node. Some areas can obviously intersect. That leads to solve the Close Enough TSP. To determine a sub-optimal trajectory for the Unmanned Aerial Vehicle, this paper presents an original and efficient strategy based on an evolutionary algorithm completed with geometrical heuristics. The performances of the algorithm are highlighted through scenarios with respectively 15 and 50 target locations. The results are analyzed with respect to the total route length. Finally, conclusions and future research directions are discussed. Full article
(This article belongs to the Special Issue Metaheuristics Algorithms and Their Applications)
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9 pages, 295 KiB  
Article
Estimating the Tour Length for the Close Enough Traveling Salesman Problem
by Debdatta Sinha Roy, Bruce Golden, Xingyin Wang and Edward Wasil
Algorithms 2021, 14(4), 123; https://doi.org/10.3390/a14040123 - 12 Apr 2021
Cited by 4 | Viewed by 3842
Abstract
We construct empirically based regression models for estimating the tour length in the Close Enough Traveling Salesman Problem (CETSP). In the CETSP, a customer is considered visited when the salesman visits any point in the customer’s service region. We build our models using [...] Read more.
We construct empirically based regression models for estimating the tour length in the Close Enough Traveling Salesman Problem (CETSP). In the CETSP, a customer is considered visited when the salesman visits any point in the customer’s service region. We build our models using as many as 14 independent variables on a set of 780 benchmark instances of the CETSP and compare the estimated tour lengths to the results from a Steiner zone heuristic. We validate our results on a new set of 234 instances that are similar to the 780 benchmark instances. We also generate results for a new set of 72 larger instances. Overall, our models fit the data well and do a very good job of estimating the tour length. In addition, we show that our modeling approach can be used to accurately estimate the optimal tour lengths for the CETSP. Full article
(This article belongs to the Special Issue Algorithms for Travelling Salesperson Problems)
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