A genetic algorithm to solve 3D traveling salesman problem with initial population based on a GRASP algorithm

Meneses, Sebastian; Cueva, Rony; Tupia, Manuel; Guanira, Miguel

doi:10.3233/jcm-160675

Cited by 5 publications

(5 citation statements)

References 11 publications

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“…Integer linear programming methods have also been proposed with ways to eliminate sub tours [32]; however, integer programming is not a suitable choice when the number of cities increases to more than one hundred. An application of a genetic algorithm for solving a 3D variation of the TSP has been presented by Meneses et al [33].…”

Section: Methodsmentioning

confidence: 99%

A Practical Methodology for Generating High-Resolution 3D Models of Open-Pit Slopes Using UAVs: Flight Path Planning and Optimization

et al. 2020

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High-resolution terrain models of open-pit mine highwalls and benches are essential in developing new automated slope monitoring systems for operational optimization. This paper presents several contributions to the field of remote sensing in surface mines providing a practical framework for generating high-resolution images using low-trim Unmanned Aerial Vehicles (UAVs). First, a novel mobile application was developed for autonomous drone flights to follow mine terrain and capture high-resolution images of the mine surface. In this article, case study is presented showcasing the ability of developed software to import area terrain, plan the flight accordingly, and finally execute the area mapping mission autonomously. Next, to model the drone’s battery performance, empirical studies were conducted considering various flight scenarios. A multivariate linear regression model for drone power consumption was derived from experimental data. The model has also been validated using data from a test flight. Finally, a genetic algorithm for solving the problem of flight planning and optimization has been employed. The developed power consumption model was used as the fitness function in the genetic algorithm. The designed algorithm was then validated using simulation studies. It is shown that the offered path optimization can reduce the time and energy of high-resolution imagery missions by over 50%. The current work provides a practical framework for stability monitoring of open-pit highwalls while achieving required energy optimization and imagery performance.

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Section: Methodsmentioning

confidence: 99%

A Practical Methodology for Generating High-Resolution 3D Models of Open-Pit Slopes Using UAVs: Flight Path Planning and Optimization

et al. 2020

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“…Meneses S., Cueva R., Tupia M., Guanira M. use a genetic algorithm to find optimal routes in three-dimensional environments (3D variation of TSP). Such evolutionary algorithms are ideal for complex tasks that require restructuring and route optimization [6]. "In the case of genetic algorithms, optimal solutions appear depending on the quality of the original population, so the theory recommends using metaheuristics to generate this population" [6].…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…Such evolutionary algorithms are ideal for complex tasks that require restructuring and route optimization [6]. "In the case of genetic algorithms, optimal solutions appear depending on the quality of the original population, so the theory recommends using metaheuristics to generate this population" [6]. Researchers use the GRASP metaheuristic algorithm to generate the initial population and genetic operators to optimize the resulting individuals [6].…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Algorithm of ant colony optimization (ACO) for 3D variation traveling salesman problem

Riabko

Заїка

Kukharchuk

et al. 2022

J. Phys.: Conf. Ser.

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The article discusses the solution of the spatial traveling salesman problem (TSP 3D variation) using Ant Colony Optimization (ACO). The traveling salesman problem considers n bridges and a matrix of pairwise distances between them. It is necessary to find such an order of visiting cities so that the total distance traveled was minimal, each city was visited exactly once and the salesman returned to the city from which he began his route. In the TSP 3D variation problem, each “city” has 3 coordinates x, y, z. The analysis of the main methods of solving, in particular, the metaheuristic algorithms to which ACO belongs, is performed. At each iteration of these methods, a new solution of the problem is built, which is based not on one, but several solutions of the population. The ACO uses an idea that is based on collecting statistical information about the best solutions. The program code is implemented in MATLAB. During computational experiments, various network topologies were randomly generated, and the number of iterations at which the optimal cycle was achieved was recorded. The execution time of the code for the TSP 3D task is almost the same as the execution time of TSP 2D. The results can be used for spatial tasks of the salesman (TSP 3D-variation), which arise in the process of 3D printing, planning UAV trajectories (UAV) in mountain conditions or multi-story urban development, road planning in multi-story buildings.

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“…This paper test the performance with 10 instances, the results in all the instances demonstrate that the proposed algorithm CTSPMRILS is more efficient than the genetic algorithms. Sebastian Meneses et al [4] intended for solving the travelling salesman problem on tridimensional environments (TSP 3D-variation). This algorithm is contributed by the genetic algorithm.…”

Section: Introductionmentioning

confidence: 99%

The Cluster Crossover Operation for The Symmetric Travelling Salesman Problem

Phu-ang

Jitkongchuen

2019

ECTI-CIT

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This paper proposed the new algorithm intended to solve a specific real-world problem, the symmetric travelling salesman problem. The proposed algorithm is based on the concept of the galaxy based search algorithm (GbSA) and embedded the new ideas called the clockwise search process and the cluster crossover operation. In the first step, the nearest neighbor algorithm introduces to generate the initial population. Then, the tabu list local search is employed to search for the new solution in surrounding areas of the initial population in the second step. The clockwise search process and the cluster crossover operation are employed to create more diversity of the new solution. Then, the final step, the hill climbing local search is utilized to increase the local search capabilities. The experiments with the standard benchmark test sets show that the proposed algorithm can be found the best average percentage deviation from the lower bound.

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A genetic algorithm to solve 3D traveling salesman problem with initial population based on a GRASP algorithm

Cited by 5 publications

References 11 publications

A Practical Methodology for Generating High-Resolution 3D Models of Open-Pit Slopes Using UAVs: Flight Path Planning and Optimization

A Practical Methodology for Generating High-Resolution 3D Models of Open-Pit Slopes Using UAVs: Flight Path Planning and Optimization

Algorithm of ant colony optimization (ACO) for 3D variation traveling salesman problem

The Cluster Crossover Operation for The Symmetric Travelling Salesman Problem

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