Analysis of the Genetic Algorithm Operators for the Node Location Problem in Local Positioning Systems

2020 ◽  
pp. 273-283 ◽  
Author(s):  
Rubén Ferrero-Guillén ◽  
Javier Díez-González ◽  
Rubén Álvarez ◽  
Hilde Pérez
Keyword(s):  
Genetic Algorithm ◽  
Location Problem ◽  
Positioning Systems ◽  
Node Location

A Hybrid Grouping Genetic Algorithm for the Multiple-Type Access Node Location Problem

2009 ◽  
pp. 376-383 ◽  
Author(s):  
O. Alonso-Garrido ◽  
S. Salcedo-Sanz ◽  
L. E. Agustín-Blas ◽  
E. G. Ortiz-García ◽  
A. M. Pérez-Bellido ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Location Problem ◽  
Grouping Genetic Algorithm ◽  
Node Location ◽  
Multiple Type

scholarly journals Hybrid Memetic Algorithm for the Node Location Problem in Local Positioning Systems

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5475 ◽  
Author(s):  
Javier Díez-González ◽  
Paula Verde ◽  
Rubén Ferrero-Guillén ◽  
Rubén Álvarez ◽  
Hilde Pérez
Keyword(s):  
Location Problem ◽  
Memetic Algorithm ◽  
Fitness Function ◽  
Hybrid Genetic Algorithm ◽  
Evolutionary Process ◽  
Function Evaluation ◽  
Problem Solution ◽  
Heuristic Solution ◽  
Positioning Systems ◽  
Node Location

Local Positioning Systems (LPS) have shown excellent performance for applications that demand high accuracy. They rely on ad-hoc node deployments which fit the environment characteristics in order to reduce the system uncertainties. The obtainment of competitive results through these systems requires the solution of the Node Location Problem (finding the optimal cartesian coordinates of the architecture sensors). This problem has been assigned as NP-Hard, therefore a heuristic solution is recommended for addressing this complex problem. Genetic Algorithms (GA) have shown an excellent trade-off between diversification and intensification in the literature. However, in Non-Line-of-Sight (NLOS) environments in which there is not continuity in the fitness function evaluation of a particular node distribution among contiguous solutions, challenges arise for the GA during the exploration of new potential regions of the space of solutions. Consequently, in this paper, we first propose a Hybrid GA with a combination of the GA operators in the evolutionary process for the Node Location Problem. Later, we introduce a Memetic Algorithm (MA) with a Local Search (LS) strategy for exploring the most different individuals of the population in search of improving the previous results. Finally, we combine the Hybrid Genetic Algorithm (HGA) and Memetic Algorithm (MA), designing an enhanced novel methodology for solving the Node Location Problem, a Hybrid Memetic Algorithm (HMA). Results show that the HMA proposed in this article outperforms all of the individual configurations presented and attains an improvement of 14.2% in accuracy for the Node Location Problem solution in the scenario of simulations with regards to the previous GA optimizations of the literature.

A simple and effective genetic algorithm for the two-stage capacitated facility location problem

2014 ◽  
Vol 75 ◽  
pp. 200-208 ◽  
Author(s):  
Diogo R.M. Fernandes ◽  
Caroline Rocha ◽  
Daniel Aloise ◽  
Glaydston M. Ribeiro ◽  
Enilson M. Santos ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Facility Location ◽  
Location Problem ◽  
Facility Location Problem ◽  
Two Stage ◽  
Capacitated Facility Location Problem ◽  
Capacitated Facility Location

Topological Design Using Genetic Algorithms

2013 ◽  
pp. 153-173
Author(s):  
Rui Manuel Morais ◽  
Armando Nolasco Pinto
Keyword(s):  
Genetic Algorithm ◽  
Programming Model ◽  
Capital Expenditure ◽  
Initial Population ◽  
Minimum Capital ◽  
Topological Design ◽  
Node Location ◽  
Telecommunications Services ◽  
Resilient Networks

The proliferation of Internet access and the appearance of new telecommunications services are originating a demand for resilient networks with extremely high capacity. Thus, topologies able to recover connections in case of failure are essential. Given the node location and the traffic matrix, the survivable topological design is the problem of determining the network topology at minimum capital expenditure such that survivability is ensured. This problem is strongly NP-hard and heuristics are traditionally used to search near-optimal solutions. The authors present a genetic algorithm for this problem. As the convergence of the genetic algorithm depends on the used operators, an analysis of their impact on the quality of the obtained solutions is presented as well. Two initial population generators, two selection methods, two crossover operators, and two population sizes are compared, and the quality of the obtained solutions is assessed using an integer linear programming model.

scholarly journals Genetic Algorithm Approach to the 3D Node Localization in TDOA Systems

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3880 ◽  
Author(s):  
Javier Díez-González ◽  
Rubén Álvarez ◽  
David González-Bárcena ◽  
Lidia Sánchez-González ◽  
Manuel Castejón-Limas ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Sensor Deployment ◽  
Selection Strategy ◽  
Ground Vehicles ◽  
Node Localization ◽  
Heuristic Solution ◽  
Positioning Systems ◽  
Genetic Algorithm Approach

Positioning asynchronous architectures based on time measurements are reaching growing importance in Local Positioning Systems (LPS). These architectures have special relevance in precision applications and indoor/outdoor navigation of automatic vehicles such as Automatic Ground Vehicles (AGVs) and Unmanned Aerial Vehicles (UAVs). The positioning error of these systems is conditioned by the algorithms used in the position calculation, the quality of the time measurements, and the sensor deployment of the signal receivers. Once the algorithms have been defined and the method to compute the time measurements has been selected, the only design criteria of the LPS is the distribution of the sensors in the three-dimensional space. This problem has proved to be NP-hard, and therefore a heuristic solution to the problem is recommended. In this paper, a genetic algorithm with the flexibility to be adapted to different scenarios and ground modelings is proposed. This algorithm is used to determine the best node localization in order to reduce the Cramér-Rao Lower Bound (CRLB) with a heteroscedastic noise consideration in each sensor of an Asynchronous Time Difference of Arrival (A-TDOA) architecture. The methodology proposed allows for the optimization of the 3D sensor deployment of a passive A-TDOA architecture, including ground modeling flexibility and heteroscedastic noise consideration with sequential iterations, and reducing the spatial discretization to achieve better results. Results show that optimization with 15% of elitism and a Tournament 3 selection strategy offers the best maximization for the algorithm.

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