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Gravity inversion of basement relief using imperialist competitive algorithm with hybrid techniques

Joolaei Amir, Arab Amiri-Alireza, Nejati Ali

Acta Geophysica

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2022

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Vol. 70, no 6

2629--2646

EN

Classically, local deterministic optimization techniques have been employed to solve such nonlinear gravity inversion problem. Nevertheless, local search methods can also be easily implemented and demonstrate higher rates of convergence; but in highly nonlinear cases such as geophysical problems, they require a reliable initial model which should be adequately close to the true model. Recently, global optimization methods have shown promising results as an alternative to classical inversion methods. Each of the global optimization algorithms has unique benefits and faults; therefore, applying different combinations of them is one of the proposed solutions for overcoming their distinct limitations. In this research, the design and implementation of the hybrid method based on a combination of the imperialist competitive algorithm (ICA) and firefly algorithm (FA) as tools of two-dimensional nonlinear modeling of gravity data and as a substitute for the local optimization methods were investigated. Hybrid of ICA and FA algorithm (known as ICAFA) is a modified form of the ICA algorithm based on the firefly algorithm. This modification results in an increase in the exploratory capability of the algorithm and improvement of its convergence rate. This inversion technique was first successfully tested on a synthetic gravity anomaly originated from a simulated sedimentary basin model both with and without the presence of white Gaussian noise (WGN). At last, the method was applied to the Bouguer anomaly from a real gravity profile in Moghan sedimentary basin (Iran). The results of this modeling were compatible with previously published works which consisted of both seismic analysis and other gravity interpretations. In order to estimate the uncertainty of solutions, several inversion runs were also conducted independently and the results were in line with the final solution.

2

A framework for optimal clustering of a greenfield distribution network area into multiple autonomous microgrids

Mojtahedzadeh S., Najafi Ravadanegh S., Haghifan M. R.

Journal of Power Technologies

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2016

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Vol. 96, nr 4

219--228

EN

Microgrids (MGs) are recognized as cores and clusters of smart distribution networks. The optimal planning and clustering of smart low-voltage distribution networks into autonomous MGs within a greenfield area is modeled and discussed in this paper. In order to form and determine the electrical boundary of MGs set, some predefined criteria such as power mismatch, supply security and load density are defined. The network includes an external grid as backup and both dispatchable and non-dispatchable Distributed Energy Resources (DERs) as MGs resources. The proposed strategy offers optimum sizing and siting of DERs and MV substations for the autonomous operation of multiple MGs simultaneously. The imperialist competitive algorithm (ICA) is used to optimize the cost function to determine the optimal linked MG clustering boundary. To evaluate the algorithm the proposed method is applied to a greenfield area which is planned to become a mixed residential and commercial town. The MGs’ optimal border, DERs location, size and type within each MG and LV feeders route are illustrated in both graphical and tabular form.

3

Designing a PID controller to control a fuel cell voltage using the imperialist competitive algorithm

Shamel A., Marefati M., Alayi R., Gholaminia B., Rohl H.

Advances in Science and Technology. Research Journal

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2016

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Vol. 10, nr 30

176--181

EN

In this article, an optimized PID controller for a fuel cell is introduced. It should be noted that we did not compute the PID controller’s coefficients based on trial-and-error method; instead, imperialist competitive algorithms have been considered. At first, the problem will be formulated as an optimization problem and solved by the mentioned algorithm, and optimized results will be obtained for PID coefficients. Then one of the important kinds of fuel cells, called proton exchange membrane fuel cell, is introduced. In order to control the voltage of this fuel cell during the changes in the charges, an optimal controller is introduced, based on the imperialist competitive algorithm. In order to apply this algorithm, the problem is written as an optimization problem which includes objectives and constraints. To achieve the most desirable controller, this algorithm is used for problem solving. Simulations confirm the better performance of proposed PID controller.

4

Applying chaotic imperialist competitive algorithm for multi-level image thresholding based on Kapur's entropy

Nejad M. R, Fartash M.

Advances in Science and Technology. Research Journal

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2016

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Vol. 10, nr 29

125--131

EN

Segmentation is one of the most important operations in image processing and computer vision. Normally, all image processing and computer vision applications are related to segmentation as a pre-processing phase. Image thresholding is one of the most useful methods for image segmentation. Various methods have been represented for image thresholding. One method is Kapur thresholding, which is based on maximizing entropy criterion. In this study, a new meta-heuristic algorithm based on imperialist competition algorithm was proposed for multi-level thresholding based on Kapur's entropy. Also, imperialist competitive algorithm is combined with chaotic functions to enhance search potency in problem space. The results of the proposed method have been compared with particle optimization algorithm and genetic algorithm. The findings revealed that the proposed method was superior to other methods.