Novel methodology for interpretation of magnetic anomalies due to two-dimensional dipping dikes using the Manta Ray Foraging Optimization

Ben, Ubong C.; Akpan, Anthony E.; Mbonu, Charles Chisom; Ebong, Ebong D.

doi:10.1016/j.jappgeo.2021.104405

Cited by 19 publications

(8 citation statements)

References 56 publications

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“…2. The method used for interpretation of gravity anomalies (Ben et al, 2021a), magnetic anomalies (Ben et al, 2021b) and satellite image enhancement (Asokan 2023). Detailed information about the method, its use, and successful solutions were given in these studies.…”

Section: Mrfo Methodsmentioning

confidence: 99%

Comparison of earthquake location parameters determined using grid search and manta ray foraging optimization

Tunçel

2023

Preprint

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Earthquake location estimation using grid search (GS) method are compared with manta ray foraging optimization (MRFO) solutions for synthetic and real earthquake data from Van city, Türkiye. Both locating methods worked very well and achieved similar results. While the coordinates (latitude and longitude) of the earthquake were obtained successfully with both methods, from the inversion of the arrival times calculated from the noisy and noise-free synthetic earthquake data, the result was obtained with some deviation in the depth parameter for the noisy data. The location parameters obtained from the inversion of the real earthquake data using GS and MRFO methods were obtained in accordance with the solutions made by other seismology centres. It was not observed that the depth parameter for the Van earthquake was in full match with the solutions of the seismology centres, but the solutions of the seismology centres given for comparison are also not consistent among themselves. The depth parameter obtained for the Van earthquake according the methods performed in this study is self-consistent and was obtained around 24 km. As a result, GS method has some disadvantageous compared with the MRFO method, for reasons such as reaching the solution tardy and needs applying gradual steps to accelerate it. The MRFO method is easy, fast, and advantageous when performed for the kinematic location of earthquake hypocentre.

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Section: Mrfo Methodsmentioning

confidence: 99%

Comparison of earthquake location parameters determined using grid search and manta ray foraging optimization

Tunçel

2023

Preprint

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“…The local exploitation is performed by the chain foraging and somersault foraging behaviors, while the global exploration is performed by the cyclone foraging behavior. This algorithm shows some optimization capabilities and is very successful in applying in some engineering domains, such as geophysics [44], energy allocation [45][46][47], image processing [48] and electric power [49]. These successful applications in the literature confirm MRFO is effective in solving different complex real-world problems.…”

Section: Introductionmentioning

confidence: 91%

Improved Manta Ray Foraging Optimization for Parameters Identification of Magnetorheological Dampers

2021

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Magnetorheological (MR) dampers play a crucial role in various engineering systems, and how to identify the control parameters of MR damper models without any prior knowledge has become a burning problem. In this study, to identify the control parameters of MR damper models more accurately, an improved manta ray foraging optimization (IMRFO) is proposed. The new algorithm designs a searching control factor according to a weak exploration ability of MRFO, which can effectively increase the global exploration of the algorithm. To prevent the premature convergence of the local optima, an adaptive weight coefficient based on the Levy flight is designed. Moreover, by introducing the Morlet wavelet mutation strategy to the algorithm, the mutation space is adaptively adjusted to enhance the ability of the algorithm to step out of stagnation and the convergence rate. The performance of the IMRFO is evaluated on two sets of benchmark functions and the results confirm the competitiveness of the proposed algorithm. Additionally, the IMRFO is applied in identifying the control parameters of MR dampers, the simulation results reveal the effectiveness and practicality of the IMRFO in the engineering applications.

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“…Therefore, it follows that not all FS problems can be solved by the FS algorithms as they are now proposed. This inspired us to study and analyze a novel FS strategy based on the Manta Ray Foraging Optimization (MRFO) meta-heuristic algorithm, which was just recently put forth.The new meta-heuristic method known as "Manta Ray Foraging Optimization" (MRFO) [5] [2] models the natural foraging behavior of manta rays. Chain foraging and somersault foraging behaviors carry out local exploitation, while cyclone foraging activity carries out worldwide exploration.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Chain foraging and somersault foraging behaviors carry out local exploitation, while cyclone foraging activity carries out worldwide exploration. The application of this approach in several technical fields, such as geophysics [5], optimal allocation of energy [23], image processing [13], and use of electric power [7], demonstrates some optimization capabilities.…”

Section: Literature Reviewmentioning

confidence: 99%

Feature Selection in MicroArray Datasets using Manta Ray Foraging Optimization Technique

Tayal¹,

Neelakantan²,

Bhargava³

2023

Proceedings of the International Conference on Application of AI and Statistical Decision Making for the Business World, ICASDM

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In this document, we apply the Manta Ray Foraging Optimization technique for Feature Selection in the genetic dataset to identify the relevant features from a high dimensional feature space. MRFO is one of the recent meta-heuristic optimization techniques which has been used for Feature Selection. presence of cancerous tumors in the patient. [10]. This document analyzes the pre existing algorithms developed in the space of Feature Selection using the MRFO algorithm in the GEM dataset. The MRFO method uses three unique search techniques in three different ways. The local search ability of the algorithm is highly aided by the chain foraging behavior; the global search ability is determined by the manta ray cyclone foraging behavior; and the local search ability and convergence rate are greatly improved by the somersault foraging behavior. This document, two separate learning classifiers were used: K-NearestNeighbors (KNN) classifier and the Random Forest Classifier were used for calculating the fitness

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Novel methodology for interpretation of magnetic anomalies due to two-dimensional dipping dikes using the Manta Ray Foraging Optimization

Cited by 19 publications

References 56 publications

Comparison of earthquake location parameters determined using grid search and manta ray foraging optimization

Comparison of earthquake location parameters determined using grid search and manta ray foraging optimization

Improved Manta Ray Foraging Optimization for Parameters Identification of Magnetorheological Dampers

Feature Selection in MicroArray Datasets using Manta Ray Foraging Optimization Technique

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