Shuffled Frog Leaping Algorithm and Wind-Driven Optimization Technique Modified with Multilayer Perceptron

Moayedi, Hossein; Bui, Dieu Tien; Ngo, Phuong Thao Thi

doi:10.3390/app10020689

Cited by 12 publications

(6 citation statements)

References 44 publications

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“…The fitness of the frogs is a measure for classifying them as the memeplexes. The SFLA pursues updating the position of the frogs in these units, and also importing new ones instead of the worst individuals [160]. The benchmark algorithms are mathematically detailed in earlier studies like [161,162] (for the SCE) and [163,164] (for the SFLA).…”

Section: The Benchmarksmentioning

confidence: 99%

An Innovative Metaheuristic Strategy for Solar Energy Management through a Neural Networks Framework

Moayedi

Mosavi

2021

Energies

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Proper management of solar energy as an effective renewable source is of high importance toward sustainable energy harvesting. This paper offers a novel sophisticated method for predicting solar irradiance (SIr) from environmental conditions. To this end, an efficient metaheuristic technique, namely electromagnetic field optimization (EFO), is employed for optimizing a neural network. This algorithm quickly mines a publicly available dataset for nonlinearly tuning the network parameters. To suggest an optimal configuration, five influential parameters of the EFO are optimized by an extensive trial and error practice. Analyzing the results showed that the proposed model can learn the SIr pattern and predict it for unseen conditions with high accuracy. Furthermore, it provided about 10% and 16% higher accuracy compared to two benchmark optimizers, namely shuffled complex evolution and shuffled frog leaping algorithm. Hence, the EFO-supervised neural network can be a promising tool for the early prediction of SIr in practice. The findings of this research may shed light on the use of advanced intelligent models for efficient energy development.

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Section: The Benchmarksmentioning

confidence: 99%

An Innovative Metaheuristic Strategy for Solar Energy Management through a Neural Networks Framework

Moayedi

Mosavi

2021

Energies

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“…The fitness of the frogs is a measure for classifying them the memeplexes. The SFLA pursues updating the position of the frogs in these units, and also, importing new ones instead of the worst individuals [126]. The benchmark algorithms are mathematically detailed in earlier studies like [127,128] (for the SCE) and [129,130] (for the SFLA).…”

Section: Data Provisionmentioning

confidence: 99%

An Innovative Metaheuristic Strategy for Solar Energy Management Through a Neural Framework

Moayedi¹,

Mosavi²

2021

Preprint

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Proper management of solar energy, as an effective renewable source, is of high importance toward sustainable energy harvesting. This paper offers a novel sophisticated method for predicting solar irradiance (SIr) from environmental conditions. To this end, an efficient metaheuristic technique, namely electromagnetic field optimization (EFO) is employed for optimizing a neural network. This algorithm quickly mines a publicly available dataset for non-linearly tuning the network parameters. To suggest an optimal configuration, five influential parameters of the EFO (i.e., NPop, R_rate, Ps_rate, P_field, and N_field) are optimized by an extensive trial and error practice. Analyzing the results showed that the proposed model can learn the SIr pattern and predict it for unseen conditions with high accuracy. Furthermore, it provided about 10% and 16% higher accuracy compared to two benchmark optimizers, namely shuffled complex evolution and shuffled frog leaping algorithm. Hence, the EFO-supervised neural network can be a promising tool for the early prediction of SIr in practice. The findings of this research may shed light on the use of advanced intelligent models for efficient energy development.

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“…After the shuffling, the quest for optimal solutions was pursued again by using knowledge in the individual memeplexes. This process of local searching and shuffling continues unless defined convergence conditions are met 57,58 and shown in Figure 6.…”

Section: Meta‐heuristic Optimization Algorithmsmentioning

confidence: 99%

A comprehensive review of modern trends in optimization techniques applied to hybrid microgrid systems

Arfeen

Sheikh

Azam

et al. 2020

Concurrency and Computation

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Microgrids have drawn substantial consideration due to high quality and reliable mix sources of electricity. This paper articulates the implication of innovative algorithms for cognitive microgrid. It perceived the algorithms that are backed by artificial intelligence (AI) are quite efficient due to the precision, convergence speed, and less computation time as compared to the conventional heuristic methods. Solar PV/Battery grid‐connected MG is modeled to achieve optimum size, supreme power quality, reduced fluctuations in voltage and frequency, reduced settling time, eliminate short transient currents, seamless power, least annual cost and high reliability as an objective function under wavering weather condition and dynamic load changes. Four broad categorizations of metaheuristic algorithms, that is, evolutionary, swarm intelligence, physics, and human intelligence‐based algorithms are well elaborated in this study. The optimal solution to the fitness function by using a hybrid optimization method also directed in the study. This paper gives deep insight to readers working in the area.

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Shuffled Frog Leaping Algorithm and Wind-Driven Optimization Technique Modified with Multilayer Perceptron

Cited by 12 publications

References 44 publications

An Innovative Metaheuristic Strategy for Solar Energy Management through a Neural Networks Framework

An Innovative Metaheuristic Strategy for Solar Energy Management through a Neural Networks Framework

An Innovative Metaheuristic Strategy for Solar Energy Management Through a Neural Framework

A comprehensive review of modern trends in optimization techniques applied to hybrid microgrid systems

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