Multivariable teaching-learning-based optimization (MTLBO) algorithm for estimating the structural parameters of the buried mass by magnetic data

This paper presents a nature-based algorithm, titled multivariable teaching-learning-based optimization (MTLBO) algorithm. MTLBO algorithm during an iterative process can estimates the best values of the buried structure (model) parameters in a multi-objective problem. The algorithm works in two computational phases: the teacher phase and the learner phase. The major purpose of the MTLBO algorithm is to modify the value of the learners and thus, improving the value of the model parameters which leads to the optimal solution. The variables of each learner (model) are the depth (z), amplitude coefficient (k), shape factor (q), angle of effective magnetization (θ) and axis location (x0) parameters. We employ MTLBO method for the magnetic anomalies caused by the buried structures with a simple geometric shape such as sphere and horizontal cylinder. The efficiency of the MTLBO is also studied by noise corruption synthetic data, as the acceptable results were obtained. We have applied the MTLBO for the interpretation of the four magnetic anomaly profiles from Iran, Brazil and India.

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Multivariable Modified Teaching Learning Based Optimization (MM-TLBO) Algorithm for Inverse Modeling of Residual Gravity Anomaly Generated by Simple Geometric Shapes

Journal of Environmental and Engineering Geophysics ◽

10.32389/jeeg20-003 ◽

2020 ◽

Vol 25 (4) ◽

pp. 463-476

Author(s):

Ata Eshaghzadeh ◽

Alireza Hajian

Keyword(s):

Gravity Anomaly ◽

Gravity Data ◽

Optimal Solution ◽

Model Parameters ◽

Major Purpose ◽

Residual Gravity ◽

Tlbo Algorithm ◽

Residual Gravity Anomaly ◽

Teaching Learning Based Optimization ◽

Teaching Learning

This paper presents an improved nature-based algorithm, namely multivariable modified teaching learning based optimization (MM-TLBO) algorithm, as in an iterative process can estimates the best values for the model parameters in a multi-objective problem. The algorithm works in two computational phases: the teacher phase and the learner phase. The major purpose of the MM-TLBO algorithm is to improve the value of the learners and thus, improving the value of the model parameters which leads to the optimal solution. The variables of each learner (model) are the radius ( R), depth ( h), shape factor ( q), density contrast ( ρ) and axis location ( x0) parameters. We apply MM-TLBO and TLBO methods for the residual gravity anomalies caused by the buried masses with a simple geometry such as spheres, horizontal and vertical cylinders. The efficiency of these methods are also tested by noise corruption synthetic data, as the acceptable results were obtained. The obtained results indicate the better performance the MM-TLBO algorithm than the TLBO algorithm. We have utilized the MM-TLBO for the interpretation of the six residual gravity anomaly profiles from Iran, USA, Sweden and Senegal. The advantage of the MM-TLBO inversion is that it can estimates the best solutions very fast without falling into local minimum and reaches to a premature convergence. The considered primary population for the synthetic and real gravity data are thirty and fifty models. The results show which this method is able to achieve the optimal responses even if a small population of learners had been considered.

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2.5‐D inversion of frequency‐domain electromagnetic data generated by a grounded‐wire source

Geophysics ◽

10.1190/1.1527076 ◽

2002 ◽

Vol 67 (6) ◽

pp. 1753-1768 ◽

Cited By ~ 37

Author(s):

Yuji Mitsuhata ◽

Toshihiro Uchida ◽

Hiroshi Amano

Keyword(s):

Frequency Domain ◽

Regularization Parameter ◽

Synthetic Data ◽

Magnetic Data ◽

Statistical Criterion ◽

Model Parameters ◽

Inversion Algorithm ◽

Gas Field ◽

Data Set ◽

Transient Electromagnetic

Interpretation of controlled‐source electromagnetic (CSEM) data is usually based on 1‐D inversions, whereas data of direct current (dc) resistivity and magnetotelluric (MT) measurements are commonly interpreted by 2‐D inversions. We have developed an algorithm to invert frequency‐Domain vertical magnetic data generated by a grounded‐wire source for a 2‐D model of the earth—a so‐called 2.5‐D inversion. To stabilize the inversion, we adopt a smoothness constraint for the model parameters and adjust the regularization parameter objectively using a statistical criterion. A test using synthetic data from a realistic model reveals the insufficiency of only one source to recover an acceptable result. In contrast, the joint use of data generated by a left‐side source and a right‐side source dramatically improves the inversion result. We applied our inversion algorithm to a field data set, which was transformed from long‐offset transient electromagnetic (LOTEM) data acquired in a Japanese oil and gas field. As demonstrated by the synthetic data set, the inversion of the joint data set automatically converged and provided a better resultant model than that of the data generated by each source. In addition, our 2.5‐D inversion accounted for the reversals in the LOTEM measurements, which is impossible using 1‐D inversions. The shallow parts (above about 1 km depth) of the final model obtained by our 2.5‐D inversion agree well with those of a 2‐D inversion of MT data.

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Minerals and ore deposits exploration using meta-heuristic based optimization on magnetic data

Contributions to Geophysics and Geodesy ◽

10.31577/congeo.2020.50.2.1 ◽

2020 ◽

Vol 50 (2) ◽

pp. 161-199

Author(s):

Mohamed GOBASHY ◽

Maha ABDELAZEEM ◽

Mohamed ABDRABOU

Keyword(s):

Mineral Exploration ◽

Random Noise ◽

Synthetic Data ◽

Ore Deposits ◽

Mine Planning ◽

Magnetic Data ◽

Real Field ◽

Model Parameters ◽

Tectonic Structures ◽

Whale Optimization

The difficulties in unravelling the tectonic structures, in some cases, prevent the understanding of the ore bodies' geometry, leading to mistakes in mineral exploration, mine planning, evaluation of ore deposits, and even mineral exploitation. For that reason, many geophysical techniques are introduced to reveal the type, dimension, and geometry of these structures. Among them, electric methods, self-potential, electromagnetic, magnetic and gravity methods. Global meta-heuristic technique using Whale Optimization Algorithm (WOA) has been utilized for assessing model parameters from magnetic anomalies due to a thin dike, a dipping dike, and a vertical fault like/shear zone geological structure. These structures are commonly associated with mineralization. This modern algorithm was firstly applied on a free-noise synthetic data and to a noisy data with three different levels of random noise to simulate natural and artificial anomaly disturbances. Good results obtained through the inversion of such synthetic examples prove the validity and applicability of our algorithm. Thereafter, the method is applied to real case studies taken from different ore mineralization resembling different geologic conditions. Data are taken from Canada, United States, Sweden, Peru, India, and Australia. The obtained results revealed good correlation with previous interpretations of these real field examples.

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Trust Enforced Cloud Service Composition Based on Teaching–Learning-Based Optimization – STORM

Journal of Circuits System and Computers ◽

10.1142/s0218126621502935 ◽

2021 ◽

pp. 2150293

Author(s):

V. Meena ◽

N. Sasikaladevi ◽

T. Suriya Praba ◽

V. S. Shankar Sriram

Keyword(s):

Service Composition ◽

Optimal Solution ◽

Cloud Service ◽

Cloud Manufacturing ◽

Test Cases ◽

Trust Value ◽

Value Maximization ◽

Teaching Learning Based Optimization ◽

Cloud Service Composition ◽

Teaching Learning

In the arena of Cloud Computing, the emergence of social networks and IoT increased the number of available services on the cloud platform, making service composition and optimal selection (SCOS) in Cloud Manufacturing (CMfg), NP-hard. The existing approaches for addressing SCOS often fail to offer assistance with maximized trust and satisfied QoS preferences. Hence, this research paper presents a novel Teach Inglea Rning-based Optimization a Lgorithm (TIROL) for achieving the optimal solution for truST enforced clOud seRvice coMposition (STORM) to assist CMfg for improving the trust value with satisfied QoS preference(s). The performance of the proposed framework has been validated using the synthetic dataset generated from different test-cases. Experimental results show that the proposed framework is reliable and outperforms the SOTA approaches in terms of trust value maximization.

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A new approach to depth determination from magnetic anomalies

Geophysics ◽

10.1190/1.1512748 ◽

2002 ◽

Vol 67 (5) ◽

pp. 1524-1531 ◽

Cited By ~ 4

Author(s):

El‐Sayed M. Abdelrahman ◽

Hesham M. El‐Araby ◽

Tarek M. El‐Araby ◽

Khalid S. Essa

Keyword(s):

Synthetic Data ◽

Vertical Component ◽

Horizontal Cylinder ◽

Magnetic Data ◽

New Approach ◽

Data Points ◽

Symmetric Points ◽

Surface Geology ◽

Effective Intensity ◽

Shallow Structure

We have developed a semiautomatic method to determine the depth to shallow and deep‐seated structures from a magnetic anomaly profile. It involves using a relationship between the depths to two coaxial sources obtained by combining observations at symmetric points with respect to the coordinate of the sources center. For five established, fixed data points, the depth to the shallow structure is determined for each preassigned depth of the deep‐seated structure. The computed depths to the shallow structure are plotted against the computed depths to the deep‐seated structure, yielding a continuous, monotonically increasing depth curve. The spacing between the observations is then modified, producing several curves. The accepted estimates for the depths to both structures are read at the common intersection of these curves. The effective intensity and the angle of magnetization of both structures are also estimated. The proposed method was tested both on noisy synthetic and real magnetic data. In the case of synthetic data, the depth curves method determined the correct depths of both coaxial and laterally offset sources. In the case of practical data (vertical component anomaly over a chromite body in the Guleman concession, Turkey), the method suggested the shape of the buried shallow structure resembles a horizontal cylinder model buried at a depth of 31 m and the shape of the buried deep seated structure resembles a dike model buried at a depth of 62 m. The estimated shape and depth of the shallow structure are in very good agreement with the results obtained from drilling and surface geology. The area appears to still hold promise for chromite exploration from the deeper structure.

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3D magnetic data-space inversion with sparseness constraints

Geophysics ◽

10.1190/1.3026538 ◽

2009 ◽

Vol 74 (1) ◽

pp. L7-L15 ◽

Cited By ~ 87

Author(s):

Mark Pilkington

Keyword(s):

Least Squares ◽

Synthetic Data ◽

Model Space ◽

Gradient Algorithm ◽

Magnetic Data ◽

Model Parameters ◽

Linear System Of Equations ◽

Data Space ◽

System Versus ◽

Sparse Inversion

I have developed an inversion approach that determines a 3D susceptibility distribution that produces a given magnetic anomaly. The subsurface model consists of a 3D, equally spaced array of dipoles. The inversion incorporates a model norm that enforces sparseness and depth weighting of the solution. Sparseness is imposed by using the Cauchy norm on model parameters. The inverse problem is posed in the data space, leading to a linear system of equations with dimensions based on the number of data, [Formula: see text]. This contrasts with the standard least-squares solution, derived through operations within the [Formula: see text]-dimensional model space ([Formula: see text] being the number of model parameters). Hence, the data-space method combined with a conjugate gradient algorithm leads to computational efficiency by dealing with an [Formula: see text] system versus an [Formula: see text] one, where [Formula: see text]. Tests on synthetic data show that sparse inversion produces a much more focused solution compared with a standard model-space, least-squares inversion. The inversion of aeromagnetic data collected over a Precambrian Shield area again shows that including the sparseness constraint leads to a simpler and better resolved solution. The degree of improvement in model resolution for the sparse case is quantified using the resolution matrix.

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Balwin-Teaching-Learning-Based Artificial Raindrop Algorithm for UAV Route Planning

Mathematical Problems in Engineering ◽

10.1155/2021/8865403 ◽

2021 ◽

Vol 2021 ◽

pp. 1-14

Author(s):

Bin Xin ◽

Fan Wang ◽

Zhibo Zhai

Keyword(s):

Route Planning ◽

Optimal Solution ◽

Unique Mode ◽

Local Optimal Solution ◽

Teaching Learning Based Optimization ◽

Teaching Learning

The prominent shortcoming of the basic artificial raindrop algorithm in UAV route planning is easily trapped into local optimal solution. In the present work, the original artificial raindrop algorithm is improved. A Balwin-teaching-learning-based artificial raindrop algorithm (BTLARA) is proposed, whereby each raindrop updates itself by using the combination of its own unique mode and Balwin-teaching-learning-based optimization pattern operator. In order to demonstrate the effectiveness of this algorithm, the UAV route planning is utilized for simulation. According to the results, the algorithm proposed in this paper significantly enhances the convergence and can obtain higher-quality navigation trace and convergence, which enables it to better avoid threat paths.

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An Analysis on the Parametric Optimization of Electrochemical Honing Process

Journal of Advanced Manufacturing Systems ◽

10.1142/s0219686720500134 ◽

2020 ◽

Vol 19 (02) ◽

pp. 249-276

Author(s):

Sunny Diyaley ◽

Shankar Chakraborthy

Keyword(s):

Removal Rate ◽

Electrochemical Machining ◽

Optimal Solution ◽

Dimensional Accuracy ◽

Cuckoo Search ◽

Machining Process ◽

De Algorithm ◽

Tlbo Algorithm ◽

Teaching Learning Based Optimization ◽

Teaching Learning

Electrochemical honing (ECH) is a nontraditional machining process hybridizing the conjoint benefits of electrochemical machining (ECM) and mechanical honing actions. In this process, maximum amount of material is removed through anodic dissolution, followed by mechanical abrasion. In present day manufacturing industries, it has found wide ranging applications, mainly in finishing of varieties of gears, due to its various advantages, like increased material removal rate, long tool life, burr-free operation, achievement of higher surface finish and dimensional accuracy, generation of no residual stress, reduced noise, less material damage, etc. In order to achieve maximum machining capability from this process, it is always recommended to set its various input parameters at their optimal operating levels. In this paper, four powerful metaheuristic algorithms, i.e. firefly algorithm, differential evolution (DE) algorithm, cuckoo search (CS) algorithm and teaching–learning-based optimization (TLBO) algorithm are applied for single as well as multi-objective optimization of pulsed-ECH (PECH) and ECH processes. It is observed that TLBO algorithm supersedes other techniques in optimizing the two ECH processes with respect to the value of the derived optimal solution, consistency of the solutions and computational speed.

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