Abstract
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.
Highlights
The definition of the location of a target is an essential fact for performing complex tasks.Traditionally, Global Navigation Satellite Systems (GNSS) have been used for providing a stable signal for many different applications such as navigation, earth observation, emergency and rescue operations or surveillance
In this paper we propose the configuration of a Hybrid Genetic Algorithm (HGA) that relies in two different phases, a deep-exploration phase followed by a heavy-intensification phase
It is true that HGA can exceed Genetic Algorithms (GA) configurations, especially in adverse scenarios, the implementation of a HGA require the adjustment of a considerable amount of hyperparameters in addition to a profound analysis on the methodologies and genetic operators selected, which can only be done experimentally
Summary
The definition of the location of a target is an essential fact for performing complex tasks. We have applied GA to the NLP in LPS [7,20,21,22,30] In these papers, we have observed that the dimensions of the space of solutions which increases with the number of sensors, the resolution of the pre-defined possible space locations for them and the complexity of the fitness function evaluation, significantly affect the stable performance of the GA.
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