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Power system stability enhancement by damping and control of Sub-synchronous torsional oscillations using whale optimization algorithm based Type-2 wind turbines

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ISA TRANSACTIONS 2021年 108卷 240-256页

作者： Kumar, Rajeev Singh, Rajveer Ashfaq, Haroon Singh, Sudhir Kumar Badoni, Manoj Jamia Millia Islamia Elect Engn Dept New Delhi 110025 India Krishna Inst Engn & Technol Elect & Elect Engn Dept Ghaziabad India Thapar Inst Engn & Technol Elect & Instrumentat Engn Dept Patiala Punjab India

This paper is aimed to demonstrate the merits of a metaheuristic swarm-based optimization technique, WOA (whale optimization algorithm), in alleviating the low-frequency torsional oscillations called SSR (Sub-synchronous resonance). The demonstration has been performed using the modified IEEE FBM (IEEE first benchmark model) aggregated with Type-2 WPP (Wind power plant). The Plant is further interlinked to the grid with series compensated lines. Use of WOA for the optimal tuning of the controller suggested in the literature to control one of the degrees of freedom, i.e., Pitch angle and external resistance connected to the rotor, has been demonstrated. The effectiveness of the proposed WOA based controller has been examined using a time-domain approach based on the dynamic response of the different segments of the test system using the Matlab software for the three different cases viz., with Type-2 WPP only, Type-2 WPP with the controller suggested in the literature and with the proposed WOA based controller. The eigenvalues, together with simulation results, reveal the potential of the proposed WOA based controller in damping the low-frequency torsional oscillations using Type-2 wind turbines. (c) 2020 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： whale optimization algorithm Wind turbine Power system control Pitch control PI controller

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Improved whale optimization algorithm based on random hopping update and random control parameter

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021年第1期40卷 363-379页

作者： Guo, Yanju Shen, Huan Chen, Lei Liu, Yu Kang, Zhilong Hebei Univ Technol Sch Elect Informat Engn Tianjin Peoples R China Tianjin Univ Commerce Sch Informat Engn Tianjin Peoples R China

whale optimization algorithm (WOA) is a relatively novel algorithm in the field of meta-heuristic algorithms. WOA can reveal an efficient performance compared with other well-established optimization algorithms, but there is still a problem of premature convergence and easy to fall into local optimal in complex multimodal functions, so this paper presents an improved WOA, and proposes the random hopping update strategy and random control parameter strategy to improve the exploration and exploitation ability of WOA. In this paper, 24 well-known benchmark functions are used to test the algorithm, including 10 unimodal functions and 14 multimodal functions. The experimental results show that the convergence accuracy of the proposed algorithm is better than that of the original algorithm on 21 functions, and better than that of the other 5 algorithms on 23 functions.

关键词： whale optimization algorithm Meta-heuristic Function optimization Random hopping update Random control parameter

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A hybrid artificial bee colony with whale optimization algorithm for improved breast cancer diagnosis

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NEURAL COMPUTING & APPLICATIONS 2021年第20期33卷 13667-13691页

作者： Stephan, Punitha Stephan, Thompson Kannan, Ramani Abraham, Ajith Karunya Inst Technol & Sci Dept Comp Sci & Engn Coimbatore Tamil Nadu India MS Ramaiah Univ Appl Sci Fac Engn & Technol Dept Comp Sci & Engn Bangalore Karnataka India Univ Teknol PETRONAS Dept Elect & Elect Engn Seri Iskandar Perak Malaysia Sci Network Innovat & Res Excellence Machine Intelligence Res Labs Washington DC USA

Breast cancer is the most common among women that leads to death if not diagnosed at early stages. Early diagnosis plays a vital role in decreasing the mortality rate globally. Manual methods for diagnosing breast cancers suffer from human errors and inaccuracy, and consume time. A computer-aided diagnosis (CAD) can overcome the disadvantages of manual methods and helps radiologists for accurate decision-making. A CAD system based on artificial neural network (ANN) optimized using a swarm-based approach can improve the accuracy of breast cancer diagnosis due to its strong prediction capabilities. Artificial bee colony (ABC) and whale optimization are metaheuristic search algorithms used to solve combinatorial optimization problems. This paper proposes a hybrid artificial bee colony with whale optimization algorithm (HAW) by integrating the exploitative employee bee phase of ABC with the bubble net attacking method of whale optimization to propose an employee bee attacking phase. In the employee bee attacking phase, employee bees use exploitation of humpback whales for finding better food source positions. The weak exploration of standard ABC is improved using the proposed mutative initialization phase that forms the explorative phase of the HAW algorithm. HAW algorithm is used in simultaneous feature selection (FS) and parameter optimization of an ANN model. HAW is implemented using backpropagation learning that includes resilient backpropagation (HAW-RP), Levenberg-Marquart (HAW-LM) and momentum-based gradient descent (HAW-GD). These hybrid variants are evaluated using various breast cancer datasets in terms of accuracy, complexity and computational time. HAW-RP variant achieved higher accuracy of 99.2%, 98.5%, 96.3%, 98.8%, 98.7% and 99.1% with low-complexity ANN model when compared to HAW-LM and HAW-GD for WBCD, WDBC, WPBC, DDSM, MIAS and INbreast, respectively.

关键词： ANNs Artificial bee colony Multilayer perceptron Levenberg– Marquardt Resilient backpropagation Momentum-based gradient descent backpropagation whale optimization algorithm

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A hybrid whale optimization algorithm for global optimization

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021年第1期14卷 431-467页

作者： Chakraborty, Sanjoy Saha, Apu Kumar Sharma, Sushmita Chakraborty, Ratul Debnath, Sudhan Iswar Chandra Vidyasagar Coll Dept Comp Sci & Engn Belonia Tripura India Natl Inst Technol Dept Comp Sci & Engn Agartala Tripura India Natl Inst Technol Dept Math Agartala Tripura India Maharaja Bir Bikram Coll Dept Stat Agartala Tripura India Maharaja Bir Bikram Coll Dept Chem Agartala Tripura India

Notwithstanding the superior performance of the whale optimization algorithm (WOA) on a wide range of optimization issues, the exploitation in WOA gets more preference during the search process, thereby compromising the solution accuracy and diversity and also increases the chance of premature convergence. In this study, a novel modified WOA (m-SDWOA) is presented where the conventional WOA is combined with the modified mutualism phase of symbiotic organisms search (SOS), DE/rand/1/bin mutation strategy of differential evolution (DE), and commensalism phase of SOS. A new selection parameter gamma is introduced to select between exploration and exploitation phases of the algorithm. This overall arrangement balances the ability of the algorithm to explore or exploit. The algorithm's efficiency is verified through 42 benchmark functions and IEEE CEC 19 test suite and comparing the results with various state-of-the-art algorithms comprising basic methods, WOA variants, and DE variants. Statistical analyses like Friedman's test, box plot comparison, and Nemenyi multiple comparison tests are employed to check the proposed algorithm's consistency and statistical superiority. Finally, four real-life engineering design problems have been solved to confirm the problem-solving capability of the proposed m-SDWOA. All these analyses demonstrate the superiority of the proposed algorithm over the compared algorithms.

关键词： whale optimization algorithm Hybrid algorithm Benchmark functions IEEE CEC 2019 functions Nemenyi multiple comparison test Engineering design problem

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Spatial bound whale optimization algorithm: an efficient high-dimensional feature selection approach

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NEURAL COMPUTING & APPLICATIONS 2021年第23期33卷 16229-16250页

作者： Too, Jingwei Mafarja, Majdi Mirjalili, Seyedali Univ Teknikal Malaysia Melaka Fac Elect Engn Durian Tunggal 76100 Melaka Malaysia Birzeit Univ Fac Engn & Technol Dept Comp Sci Birzeit Palestine Torrens Univ Australia Ctr Artificial Intelligence Res & Optimizat Brisbane Qld 4006 Australia Yonsei Univ Yonsei Frontier Lab Seoul South Korea King Abdulaziz Univ Jeddah Saudi Arabia

Selecting a subset of candidate features is one of the important steps in the data mining process. The ultimate goal of feature selection is to select an optimal number of high-quality features that can maximize the performance of the learning algorithm. However, this problem becomes challenging when the number of features increases in a dataset. Hence, advanced optimization techniques are used these days to search for the optimal feature combinations. whale optimization algorithm (WOA) is a recent metaheuristic that has successfully applied to different optimization problems. In this work, we propose a new variant of WOA (SBWOA) based on spatial bounding strategy to play the role of finding the potential features from the high-dimensional feature space. Also, a simplified version of SBWOA is introduced in an attempt to maintain a low computational complexity. The effectiveness of the proposed approach was validated on 16 high-dimensional datasets gathered from Arizona State University, and the results are compared with the other eight state-of-the-art feature selection methods. Among the competitors, SBWOA has achieved the highest accuracy for most datasets such as TOX_171, Colon, and Prostate_GE. The results obtained demonstrate the supremacy of the proposed approaches over the comparison methods.

关键词： whale optimization algorithm Feature selection Data mining Classification High Dimensional Data optimization Benchmark WOA Swarm intelligence Evolutionary

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Code Smell Detection Using whale optimization algorithm

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Computers, Materials & Continua 2021年第8期68卷 1919-1935页

作者： Moatasem M.Draz Marwa S.Farhan Sarah N.Abdulkader M.G.Gafar Department of Software Engineering Faculty of Computers and InformationKafrelsheikh UniversityKafr ElsheikhEgypt Faculty of Informatics and Computer Science British University in EgyptCairoEgypt Department of Information Systems Faculty of Computers and Artificial IntelligenceHelwan UniversityCairoEgypt Department of Computer Science Faculty of Computers and Artificial IntelligenceHelwan UniversityCairoEgypt Faculty of Computer Studies Arab Open UniversityCairoEgypt Department of Computer Science College of Science and Humanities in Al-SulailPrince Sattam bin Abdulaziz UniversityKharjSaudi Arabia Department of Machine Learning and Information Retrieval Faculty of Artificial IntelligenceKafrelsheikh UniversityKafr ElsheikhEgypt

Software systems have been employed in many fields as a means to reduce human efforts;consequently,stakeholders are interested in more updates of their *** smells arise as one of the obstacles in the software *** are characteristics of software source code that indicate a deeper problem in *** smells appear not only in the design but also in software *** smells introduce bugs,affect software maintainability,and lead to higher maintenance *** code smells can be formulated as an optimization problem of finding the best detection *** researchers have recommended different techniques to improve the accuracy of code smell detection,these methods are still unstable and need to be *** research has sought only to discover a few at a time(three or five types)and did not set rules for detecting their *** research improves code smell detection by applying a search-based technique;we use the whale optimization algorithm as a classifier to find ideal detection *** this algorithm,the Fisher criterion is utilized as a fitness function to maximize the between-class distance over the withinclass *** proposed framework adopts if-then detection rules during the software development life *** rules identify the types for both medium and large *** are conducted on five open-source software projects to discover nine smell types that mostly appear in *** proposed detection framework has an average of 94.24%precision and 93.4%*** accurate values are better than other search-based algorithms of the same *** proposed framework improves code smell detection,which increases software quality while minimizing maintenance effort,time,and ***,the resulting classification rules are analyzed to find the software metrics that differentiate the nine code smells.

关键词： Software engineering intelligence search-based software engineering code smell detection software metrics whale optimization algorithm fisher criterion

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Multilevel thresholding image segmentation based on improved volleyball premier league algorithm using whale optimization algorithm

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MULTIMEDIA TOOLS AND APPLICATIONS 2021年第8期80卷 12435-12468页

作者： Abd Elaziz, Mohamed Nabil, Neggaz Moghdani, Reza Ewees, Ahmed A. Cuevas, Erik Lu, Songfeng Zagazig Univ Dept Math Fac Sci Zagazig Egypt Univ Sci & Technol Oran Mohammed Boudiaf USTO MB Fac Math & Informat Dept InformatLab SIMPA BP 1505 El Mnaouer 31000 Oran Algeria Persian Gulf Univ Ind Management Dept Boushehr Iran Damietta Univ Dept Comp Dumyat Egypt Univ Guadalajara CUCEI Dept Elect Av Revoluc 1500 Guadalajara 44430 Jalisco Mexico Huazhong Univ Sci & Technol Sch Cyber Sci & Engn Wuhan 430074 Peoples R China

Multilevel thresholding image segmentation has received considerable attention in several image processing applications. However, the process of determining the optimal threshold values (as the preprocessing step) is time-consuming when traditional methods are used. Although these limitations can be addressed by applying metaheuristic methods, such approaches may be idle with a local solution. This study proposed an alternative multilevel thresholding image segmentation method called VPLWOA, which is an improved version of the volleyball premier league (VPL) algorithm using the whale optimization algorithm (WOA). In VPLWOA, the WOA is used as a local search system to improve the learning phase of the VPL algorithm. A set of experimental series is performed using two different image datasets to assess the performance of the VPLWOA in determining the values that may be optimal threshold, and the performance of this algorithm is compared with other approaches. Experimental results show that the proposed VPLWOA outperforms the other approaches in terms of several performance measures, such as signal-to-noise ratio and structural similarity index.

关键词： Image segmentation Multilevel thresholding Swarm algorithm Volleyball premier league algorithm whale optimization algorithm

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Automatic breast cancer detection based on optimized neural network using whale optimization algorithm

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INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY 2021年第1期31卷 425-438页

作者： Fang, Hong Fan, Hongyu Lin, Shan Qing, Zhang Sheykhahmad, Fatima Rashid Dalian Univ Affiliated Zhongshan Hosp Dept Breast Dalian Peoples R China Islamic Azad Univ Ardabil Branch Young Researchers & Elite Club Ardebil Iran

Breast cancer is the second deadliest type of cancer. Early detection of breast cancer can considerably improve the effectiveness of treatment. A significant early sign of breast cancer is the mass. However, separating the cancerous masses from the normal portions of the breast tissue is usually a challenge for radiologists. Recently, because of the availability of high-accuracy computing, computer-aided detection systems based on image processing have become capable of accurately diagnosing the various types of cancers. The main purpose of this study is to utilize a powerful image segmentation method for the diagnosis of cancerous regions through mammography, based on a new configuration of the multilayer perceptron (MLP) neural network. The most popular method for minimizing the errors in an MLP neural network is backpropagation. However, this method has certain drawbacks, such as a low convergence speed and becoming trapped at the local minimum. In this study, a new training algorithm based on the whale optimization algorithm is proposed for the MLP network. This algorithm is capable of solving various problems toward the current algorithms for the analyzed systems. The proposed method is validated on the Mammographic Image Analysis Society database, which contains 322 digitized mammography images, and the Digital Database for Screening Mammography, which contains approximately 2500 digitized mammography images. To assess the detection performance of the proposed system, the correct detection rate, percentage of identification with false acceptance, and percentage of identification with false rejection were evaluated and compared using various methods. The results indicate that the proposed method is highly efficient and yields significantly better accuracy compared with other methods.

关键词： breast cancer computer-aided diagnosis image segmentation neural networks whale optimization algorithm

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A Backbone whale optimization algorithm Based on Cross-stage Evolution 13th

A Backbone Whale Optimization Algorithm Based on Cross-stage...

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13th International Conference on Swarm Intelligence (ICSI)

作者： Yang, Xin Wang, Limin Zhang, Zhiqi Han, Xuming Yue, Lin Jinan Univ Guangzhou Peoples R China

ISBN: (纸本)9783031096778;9783031096761

The swarm intelligent algorithms (SIs) are effective and widely used, while the balance between exploitation and exploration directly affects the accuracy and efficiency of algorithms. To cope with this issue, a backbone whale optimization algorithm based on cross-stage evolution (BWOACS) is proposed. BWOACS is mainly composed of three parts: (1) adopts the density peak clustering (DPC) method to actively divide the population into several sub-populations, generates the backbone representatives (BR) during backbone construction stage;(2) determines the deviation placement (DP) by constructing the co-evolution operators (CE), the search space expansion operators (SE) and the guided transfer operators (GT) during bionic evolution strategy stage;(3) realises the bionic optimisation through DP during backbone representatives guiding co-evolution stage. To verify the accuracy and performance of BWOACS, we compare BWOACS with other variants on 9 IEEE CEC 2017 benchmark problems. Experimental results indicate that BWOACS has better accuracy and convergence speed than other algorithms.

关键词： whale optimization algorithm Density peak clustering Bionic evolution strategy

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Multiple Populations-Based whale optimization algorithm for Solving Multicarrier NOMA Power Allocation Strategy Problem 18th

Multiple Populations-Based Whale Optimization Algorithm for ...

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18th International Conference on Intelligent Computing (ICIC)

作者： Liang, Zhiwei Luo, Qifang Zhou, Yongquan Guangxi Univ Nationalities Coll Elect Informat Nanning 530006 Peoples R China Guangxi High Sch Key Lab Complex Syst & Computat Intelligence Nanning 530006 Peoples R China

ISBN: (纸本)9783031138324;9783031138317

In this paper, a multiple populations-based whale optimization algorithm (FWOA) is proposed to enhance the accuracy of the classical whale optimization algorithm (WOA) for multicarrier NOMA power allocation strategy problem. Weight parameters (adaptive weight, exploration weight, exploration power) and modified exploitation phase are also introduced to enhance the classical WOA. The results of the above problem are compared with the result of other six classical algorithms, including the classical whale optimization algorithm (WOA), Particle swarm optimization (PSO), Seagull optimization algorithm (SOA), Grey wolf optimization (GWO), Sooty tern optimization algorithm (STOA) and Salp Swarm algorithm (SSA). Experiments show that FWOA is competitive with other algorithms.

关键词： Metaheuristic algorithm whale optimization algorithm Multicarrier NOMA power allocation strategy Multiple population-based optimization

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