Manta ray foraging optimization algorithm–based feedforward neural network for electric energy consumption forecasting

Duman, Serhat; Dalcalı, Adem; Özbay, Harun

doi:10.1002/2050-7038.12999

Cited by 10 publications

(4 citation statements)

References 55 publications

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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: 90%

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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Section: Introductionmentioning

confidence: 90%

Improved Manta Ray Foraging Optimization for Parameters Identification of Magnetorheological Dampers

2021

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“…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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“…Duman et al [73] presented a combined model comprising the feedforward NN (FFNN) and MRFO for forecasting electric energy consumption. The MRFO trains the FFNN.…”

Section: ) Mrfo With the Neural Networkmentioning

confidence: 99%

Manta Ray Foraging Optimization Algorithm: Modifications and Applications

et al. 2023

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The novel metaheuristic manta ray foraging optimization (MRFO) algorithm is based on the smart conduct of manta rays. The MRFO algorithm is a newly developed swarm-based metaheuristic approach that emulates the supportive conduct performed by manta rays in search of food. The MRFO algorithm efficiently resolves several optimization difficulties in various domains due to its ability to provide an equilibrium between global and local searches during the search procedure, resulting in nearly optimal results. Thus, researchers have developed several variants of MRFO since its introduction. This paper provides an in-depth examination of recent MRFO research. First, the paper introduces the natural inspiration context of MRFO and its conceptual optimization framework, and then MRFO modifications, hybridizations, and applications across different domains are discussed. Finally, a meta-analysis of the developments of the MRFO is presented along with the possible future research directions. This study can be useful for researchers and practitioners in optimization, engineering design, machine learning, scheduling, image processing, and other fields.

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Manta ray foraging optimization algorithm–based feedforward neural network for electric energy consumption forecasting

Abstract: As a consequence of the growing world population along with the rapid developments in technology, electric energy consumption is increasing. Considering

Cited by 10 publications

References 55 publications

Improved Manta Ray Foraging Optimization for Parameters Identification of Magnetorheological Dampers

Improved Manta Ray Foraging Optimization for Parameters Identification of Magnetorheological Dampers

Feature Selection in MicroArray Datasets using Manta Ray Foraging Optimization Technique

Manta Ray Foraging Optimization Algorithm: Modifications and Applications

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