Assessing the Stability of the Hybrid PSO-GA Algorithm in Magnetic Model Parameter Estimation Compared to Two Separate Approaches

Authors

1 Department of Petroleum, Mining and Material Engineering, Faculty of Civil and Earth Resources Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran

2 Department of Physics, Karaj Branch, Islamic Azad University, Karaj, Iran

3 Department of Physics, Central Tehran Branch, Islamic Azad University, Tehran, Iran.

Abstract

In this study, the stability of the combined PSO-GA algorithm in estimating magnetic model parameters is evaluated and compared with two other algorithms: Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The PSO algorithm is employed to enhance the action vector, while the GA algorithm is applied to rectify the decision vectors. This amalgamation of algorithms, utilizing genetic operators, enhances exploration and exploitation capabilities. The optimization algorithm exhibits the potential to be applied in the exploration and estimation of mineral reservoirs, offering a rapid method for simulating magnetic anomalies based on ideal geological models. Moreover, in geophysical investigations, it is customary to employ modeling techniques using standard geometric shapes like spheres, cylinders, vertical prisms, dikes, and similar forms to assess magnetic anomaly attributes. Furthermore, in current research, the algorithm's performance is examined by introducing Gaussian white noise to synthetic data, demonstrating its effectiveness even when faced with noise levels as high as 25%. Additionally, authentic airborne magnetic data from the Basiran region in South Khorasan province are applied to validate the model, confirming its consistency with geological findings.

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References

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Advanced Applied Geology
Volume 14, Issue 3
In progress
October 2024
Pages 755-784

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