Hybrid PSO-GA Algorithm for Estimation of Magnetic Anomaly Parameters Due to Simple Geometric Structures

Sohouli, A. N.; Molhem, H.; Zare-Dehnavi, N.

doi:10.1007/s00024-022-03048-2

Cited by 9 publications

(4 citation statements)

References 54 publications

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“…The method uses a particle swarm algorithm for magnetic data improvement and a genetic algorithm for model parameter estimation. The algorithm can offer valuable results for estimating model parameters under a 25% noise level [13].…”

Section: Related Workmentioning

confidence: 99%

“…By introducing the idea of updating the position information in the GWO algorithm, the PSO's search capability is optimized so that the particles in space can expand the search space and enhance the search effort, thus finding the optimal solution more efficiently and accurately [21]. The updated formula for the particles ij V and ij X after the introduction of the GWO idea is shown in equation (13).…”

Section: B Wsn Coverage Optimization Model For the Gwpso Hybrid Optim...mentioning

confidence: 99%

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Hybrid Particle Swarm Optimization-based Modeling of Wireless Sensor Network Coverage Optimization

Kou¹,

Wei²

2023

IJACSA

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To address the problem of insufficient coverage of WSN and poor network coverage in obstacle environments, the study proposes an improved particle swarm optimization (PSO) combined with a hybrid grey wolf algorithm. The speed and position of the PSO particle's search for superiority are enhanced through the guiding nature of the superior wolf in the grey wolf optimization (GWO), thus the convergence speed and search precision are improved. Based on this, the study applies the improved PSO to a wireless sensor networks (WSO) coverage optimization model and uses model comparison to test the effectiveness and superiority of the algorithm. According to the results, the node network coverage of PSO, genetic algorithm (GA), data envelopment analysis (DEA), GWO, and grey wolf particle swarm optimization (GWPSO) reach 85.97%, 87.24%, 88.76%, 89.31%, and 91.05% respectively in the trapezoidal obstacle environment. And the node network coverage of the research-designed GWPSO algorithm reaches the highest value of its kind. This shows that the research-designed GWPSO has superior performance in the optimization control of sensor coverage deployment compared with similar algorithms. The design provides a new path for optimizing wireless sensor node network coverage.

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Section: Related Workmentioning

confidence: 99%

Section: B Wsn Coverage Optimization Model For the Gwpso Hybrid Optim...mentioning

confidence: 99%

Hybrid Particle Swarm Optimization-based Modeling of Wireless Sensor Network Coverage Optimization

Kou¹,

Wei²

2023

IJACSA

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“…Numerous studies have been conducted on developing various optimization algorithms, especially those based on natural phenomena, and their application to solve optimization problems in various fields of science and engineering (Nama et al., 2017). These algorithms have also been used to solve ill‐posed magnetic inverse problems, including Ant Colony Optimization (Liu et al., 2015; Srivastava et al., 2014), Barnacles Mating Optimization (Ai et al., 2022), Bat Optimization Algorithm (Essa & Diab, 2022), Differential Evolution (DE) (Balkaya et al., 2017; Du et al., 2021), Differential Search (Balkaya & Kaftan, 2021; Özyalın, 2023), Genetic Algorithm (Kaftan, 2017; Montesinos et al., 2016; Sohouli et al., 2022), Genetic‐Price Algorithm (GPA) (Di Maio et al., 2020), Gray Wolf Optimization (Agarwal et al., 2018), Hunger Games Search Algorithm (Ai et al., 2023), Manta Ray Foraging Optimization Algorithm (MRFO) (Ben, Ekwok, et al, 2022; Ben et al., 2021), Particle Swarm Optimization (PSO) (Ekinci et al., 2020; Ekwok et al., 2023; Liu et al., 2018; Srivastava & Agarwal, 2010), Social Spider Optimization (Ben, Akpan, et al., 2022), Whale Optimization Algorithm (WOA) (Divakar et al., 2018; Gobashy et al., 2020) and Simulated Annealing (SA) (Biswas et al., 2022; Biswas & Rao, 2021; Shinu & Dubey, 2023). The choice of the most appropriate algorithm for a given optimization problem may depend on several factors, such as the complexity of the problem, the size of the search space, the required precision, and the available computational resources (Dragoi & Dafinescu, 2021).…”

Section: Introductionmentioning

confidence: 99%

Fault Structure Reconstruction From Magnetic Anomalies Using an Improved Backtracking Search Optimization Algorithm

Balkaya,

Ekinci,

et al. 2024

Earth and Space Science

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Fault structure reconstruction is an important aspect of magnetic prospecting as it contributes to understanding tectonic history, hazard assessment, and infrastructure planning. Metaheuristics have proven useful in estimating fault structure parameters from geophysical anomalies. Recently, a modified version of the Backtracking Search Optimization Algorithm (BSA), named mBSA, has been proposed that combines mutation steps of BSA and Differential Evolution (DE) algorithms to achieve a better balance between exploration and exploitation. Here, we present imBSA as an efficient approach that builds on the improvements of mBSA. It uses an adaptive amplitude control factor in the mutation step derived from a normally distributed random number obtained via an exponential function. The performances of these approaches were tested on some widely used benchmark functions such as Rosenbrock, Rastrigin, Griewank, and Himmelblau. Synthetic magnetic fault anomalies were evaluated with and without noise, as well as three real anomalies from the Dehri (India), Perth Basin, and Lachlan Fold Belt (Australia). Prior to the optimizations, the solvability of the model parameters was examined using some error topography images. The obtained solutions in the synthetic cases were evaluated using principal component analysis. In addition, a novel visualization option for high‐dimensional optimization problems was introduced. The experiments showed that imBSA has a faster convergence rate and better optimization statistics compared to BSA and mBSA. Therefore, it is believed to be an efficient and good alternative tool to widely used metaheuristics such as Particle Swarm Optimization and DE in explaining geophysical anomalies and optimization problems in other geoscientific disciplines.

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“…To remove undesired regional anomaly from the measured geomagnetic anomalies, a second moving average technique (SMA) (Essa, 2019;Essa et al, 2021) was used in both synthetic and real data experiments, and satisfactory results were obtained. Additionally, the ability of the HGS to balance exploration and exploitation stages was compared to that of standard Particle Swarm Optimization (SPSO), which is frequently used as a global optimizer in geophysical anomaly inversion problems (Ekinci, Balkaya, & Göktürkler, 2020;Fernández-Martínez et al, 2010;Göktürkler & Balkaya, 2012;Pace et al, 2021;Pallero et al, 2015Pallero et al, , 2017Pallero et al, , 2021Roy et al, 2021b;Santos, 2010;Sohouli et al, 2022). Applications showed that HGS, which outperforms SPSO in the cases presented, is a powerful inversion tool for giving insights into the subsurface nature of ore/mineral deposits using geomagnetic anomalies.…”

mentioning

confidence: 99%

Inversion of Geomagnetic Anomalies Caused by Ore Masses Using Hunger Games Search Algorithm

Ai,

Ekinci,

Balkaya

et al. 2023

Earth and Space Science

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Geomagnetic anomaly interpretation through inversion procedures often yields useful results in determining the key details of ore masses. However, the problem is complicated due to the known ambiguous phenomena of the inversion process. Thus, details such as location, depth and shape can only be estimated using an efficient algorithm. To this end, we presented here a novel global optimization algorithm called Hunger Games Search (HGS) for the inversion of geomagnetic anomalies caused by ore masses. HGS is a well‐organized metaheuristic inspired by the hunger‐driven instincts and social behavioral decisions of animals. This is the first work in the literature to introduce this optimizer for the inversion of geophysical anomalies. We revealed the model parameter dependencies and the optimum control parameter values of the algorithm by performing some modal analyses and parameter tuning studies, respectively. The capability of HGS was demonstrated on synthetically produced geomagnetic responses and on three real anomalies obtained from some exploration fields in India and the USA. Uncertainty appraisal studies showed the solidity of the outputs of the algorithm. Moreover, some comparative studies with the fine‐tuned standard Particle Swarm Optimization, a commonly used tool for the inversion of geophysical anomalies, indicated that HGS algorithm provides better results in terms of convergence characteristics and solution stabilities in the presented inverse problem. We therefore recommend the use of this metaheuristic in model parameter estimation studies when dealing with this kind of geophysical potential field problems.

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Hybrid PSO-GA Algorithm for Estimation of Magnetic Anomaly Parameters Due to Simple Geometric Structures

Cited by 9 publications

References 54 publications

Hybrid Particle Swarm Optimization-based Modeling of Wireless Sensor Network Coverage Optimization

Hybrid Particle Swarm Optimization-based Modeling of Wireless Sensor Network Coverage Optimization

Fault Structure Reconstruction From Magnetic Anomalies Using an Improved Backtracking Search Optimization Algorithm

Inversion of Geomagnetic Anomalies Caused by Ore Masses Using Hunger Games Search Algorithm

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