Modified Search Strategies Assisted Crossover Whale Optimization Algorithm with Selection Operator for Parameter Extraction of Solar Photovoltaic Models

Xiong, Guojiang; Zhang, Jing; Shi, Dongyuan; Zhu, Lin; Yuan, Xufeng; Yao, Gang

doi:10.3390/rs11232795

Cited by 29 publications

(13 citation statements)

References 53 publications

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“…Some contributions focused on different topics in solar energy applications, such as the research articles from Alkadri at al. [7] and Xiong et al [1]. The first paper describes an investigation on the potential application of attribute information stored in point cloud data to support a new computational method for a voxel-based design approach based on shading performance criteria.…”

Section: Brief Discussion Of the Published Articles In The Special Issuementioning

confidence: 99%

“…The first paper describes an investigation on the potential application of attribute information stored in point cloud data to support a new computational method for a voxel-based design approach based on shading performance criteria. Xiong et al [1] improved a variant of the whale optimization algorithm to extract PV model parameters. This Special Issue encompasses two research articles focused on remote sensing applied to wind energy resource assessment.…”

Section: Brief Discussion Of the Published Articles In The Special Issuementioning

confidence: 99%

“…This Special Issue is comprised of ten research papers [1][2][3][4][5][6][7][8][9][10] and one letter [11]. Figure 1 shows a pictorial word cloud of the Abstract contents of the eleven manuscripts, where the strong multi-disciplinary character of the remote sensing approaches applied to renewable energy is apparent.…”

Section: Overview Data and Methodsmentioning

confidence: 99%

“…Table 1 lists a summary of the remote sensing data and methods used and the energy resources investigated. Eight papers describe remote sensing approaches for solar energy applications; three of them focus on PV power generation [1,6,10]. Two articles are focused on wind energy, one using LiDAR measurements from the ground [3] and the other using scatterometers operating in several satellite missions [9].…”

Section: Overview Data and Methodsmentioning

confidence: 99%

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Editorial for the Special Issue: Assessment of Renewable Energy Resources with Remote Sensing

Martins

2020

Remote Sensing

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The development of renewable energy sources plays a fundamental role in the transition towards a low carbon economy. Considering that renewable energy resources have an intrinsic relationship with meteorological conditions and climate patterns, methodologies based on the remote sensing of the atmosphere are fundamental sources of information to support the energy sector in planning and operation procedures. This Special Issue is intended to provide a highly recognized international forum to present recent advances in remote sensing to data acquisition required by the energy sector. After a review, a total of eleven papers were accepted for publication. The contributions focus on solar, wind, and geothermal energy resource. This editorial presents a brief overview of each contribution.

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Section: Brief Discussion Of the Published Articles In The Special Issuementioning

confidence: 99%

Section: Brief Discussion Of the Published Articles In The Special Issuementioning

confidence: 99%

Section: Overview Data and Methodsmentioning

confidence: 99%

Section: Overview Data and Methodsmentioning

confidence: 99%

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Editorial for the Special Issue: Assessment of Renewable Energy Resources with Remote Sensing

Martins

2020

Remote Sensing

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“…Abdel-Basset et al developed modified version of WOA (MWOA) 20 to assess the vulnerability of Merkle-Hellman knapsack cryptosystems. Xiong et al proposed (MCSWOA) 21 using two mutation strategies and crossover from DE, used the new method to extract parameters of the photovoltaic cell. Dhabal and Saha presented differential evolution-based WOA (DEWOA), 22 integrating DE with WOA, and used the new algorithm for image enhancement purposes.…”

mentioning

confidence: 99%

HSWOA: An ensemble of hunger games search and whale optimization algorithm for global optimization

Chakraborty¹,

Saha

Chakraborty

et al. 2021

Int J Intell Syst

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The search for food stimulated by hunger is a common phenomenon in the animal world. Mimicking the concept, recently, an optimization algorithm Hunger Games Search (HGS) has been proposed for global optimization. On the other side, the Whale Optimization Algorithm (WOA) is a commonly utilized natureinspired algorithm portrayed by a straightforward construction with easy parameters imitating the hunting behavior of humpback whales. However, due to minimum exploration of the search space, WOA has a high chance of trapping into local solutions, and more exploitation leads it towards premature convergence.The concept of hunger from HGS is merged with the food searching techniques of the whale to lessen the inherent drawbacks of WOA. Two weights of HGS are adaptively designed for every whale using the respective hunger level for balancing search strategies. Performance verification of the proposed hunger search-based whale optimization algorithm (HSWOA) is done by comparing it with 10 state-of-the-art algorithms, including three very recently developed algorithms on 30 classical benchmark functions. Comparison with some basic algorithms, recently modified algorithms, and WOA variants is performed using IEEE CEC 2019 function set. Statistical performance of the proposed algorithm is verified with Friedman's test, boxplot analysis, and Nemenyi multiple comparison test. The operating speed of the algorithm is determined and tested with complexity analysis and convergence analysis. Finally, seven real-world engineering problems are solved and compared with a list of metaheuristic algorithms. Numerical and statistical performance comparison with state-of-the-art algorithms confirms the efficacy of the newly designed algorithm.

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Characterization of abnormalities in breast cancer images using nature‐inspired metaheuristic optimized convolutional neural networks model

Oyelade

Ezugwu

2021

Concurrency and Computation

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Convolutional neural networks (CNN) are deep learning models widely reported as performing well in several image classification tasks. Training the networks for optimal performance is considered an NP-hard problem. The combination of a metaheuristic algorithm with CNN has been proposed to address this problem because the metaheuristic algorithms have efficient performance, parameter tuning, and stagnation prevention methods. Moreover, there is now an increasing need to apply the best performing metaheuristic algorithm to optimize the parameters, training and learning rate of the CNN models. This study aims to investigate the best performing metaheuristic algorithm for fine-tuning the weights, biases, and hyperparameters of CNN networks for solving the problem of characterization of abnormalities in breast images. Furthermore, hybrid models consisting of a CNN architecture and five representative metaheuristic algorithms to efficiently detect breast cancer abnormalities are presented. The adopted approach involves training a CNN network using genetic algorithm (GA), whale optimization algorithm (WOA), multiverse optimizer (MVO), satin bower optimization (SBO), and life choice-based optimization (LCBO) algorithms to optimize only weights and bias of the model. Two categories of experiments were carried out: the first involved training of the CNN model, while the second involved optimizing the training of CNN model with metaheuristic algorithms. A comparative analysis of the impact of the proposed GA, WOA, MVO, SBO, and LCBO metaheuristic algorithms on the performance of the CNN architecture was carried out. Empirical and statistical analyses are presented to validate further the findings obtained in the study. Results obtained showed that MVO, SBO and LCBO outperformed GA, WOA. The classification accuracy obtained for CNN-GA, CNN-WOA, CNN-MVO, CNN-SBO, and CNN-LCBO at the fifth epoch was 0.76, 0.75, 0.84, 0.80, and 0.86, respectively. At the same time, the traditional CNN achieved an accuracy of 0.66. The outcome of this study showed that contrary to widespread practice, physics-based, biology-based, and human-based optimization algorithms promise better performance parameter tuning for CNN as compared with evolutionary-based and swarm-based metaheuristic algorithms.

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Modified Search Strategies Assisted Crossover Whale Optimization Algorithm with Selection Operator for Parameter Extraction of Solar Photovoltaic Models

Cited by 29 publications

References 53 publications

Editorial for the Special Issue: Assessment of Renewable Energy Resources with Remote Sensing

Editorial for the Special Issue: Assessment of Renewable Energy Resources with Remote Sensing

HSWOA: An ensemble of hunger games search and whale optimization algorithm for global optimization

Characterization of abnormalities in breast cancer images using nature‐inspired metaheuristic optimized convolutional neural networks model

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