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Small signal stability enhancement of a large scale power system using a bio-inspired whale optimization algorithm

Protection and Control of Modern Power Systems 2021年第1期6卷 443-459页

作者： Butti Dasu Sivakumar Mangipudi Srinivasarao Rayapudi Department of EEE Gudlavalleru Engineering CollegeGudlavlleruKrishna(Dt.)Andhra Pradesh521301India Department of EEE University College of EngineeringJNTUKKakinadaEast Godavari(Dt.)Andhra Pradesh533001India

A whale optimization algorithm(WOA)-based power system stabilizer(PSS)design methodology on modified single machine infinite bus(MSMIB)and multi-machine systems to enhance the small-signal stability(SSS)of the power system is *** PSS design methodology is implemented using an eigenvalue(EV)-based objective *** performance of the WOA is tested with several CEC14 and CEC17 test functions to investigate its potential in optimizing the complex mathematical *** New England 10-generator 39-bus system and the MSMIB system operating at various loading conditions are considered as the test systems to examine the proposed *** simulation results are obtained which validate the effectiveness of the proposed WOA method when compared with other algorithms.

关键词： Small-signal stability Power system stabilizer Stability Modified single machine infinite bus system Multi-machine system whale optimization algorithm Eigenvalues

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Two-stage damage identification method based on fractal theory and whale optimization algorithm

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ADVANCES IN STRUCTURAL ENGINEERING 2022年第11期25卷 2364-2381页

作者： Huang, Minshui Wan, Zihao Cheng, Xihao Xu, Zian Lei, Yongzhi Pan, Deng Wuhan Inst Technol Sch Civil Engn & Architecture 693 Xiongchu Ave Wuhan 430073 Peoples R China

Structural damage identification based on time domain method of vibration response has been widely developed in the recent decades, however, it still confronts some difficulties, such as measurement noise and model error. This paper proposes a novel two-stage damage identification method based on fractal dimension and whale optimization algorithm (WOA). In this study, based on vibration data, the difference in curvature of fractal dimension (DCFD) is used as the damage index to identify the location of suspicious damage elements in the first stage. A new objective function is proposed based on the curvature of fractal dimension (CFD) of acceleration signal, and the WOA is used to estimate the severity of the suspicious damaged element in the second stage. Firstly, the validity of the proposed method is verified by a numerical simply supported beam, and the results exhibit good damage identification ability. Then different noise levels (5% similar to 20%) are introduced into the dynamic responses to verify its robustness, the result shows that the method is of good anti-noise ability in the first stage. Although the second stage is slightly sensitive to noise, it can still effectively identify the severity of damage. Secondly, the vibration testing of a steel I-beam is designed to verify the rationality of the method in the application of actual structure. Finally, based on the simulated vibration test data of the I-40 Bridge, the applicability of the method to complex civil structure is verified, which shows that the method still has good ability to identify the location and severity of damage in complex structure and is of great significance in practical application.

关键词： Damage identification time domain fractal dimension whale optimization algorithm two-stage damage identification method

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Environmental cold chain distribution center location model in the semiconductor supply chain: A hybrid arithmetic whale optimization algorithm

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COMPUTERS & INDUSTRIAL ENGINEERING 2024年 187卷

作者： Wang, Xiaolin Zhan, Liyi Zhang, Yong Fei, Teng Tseng, Ming-Lang Tianjin Univ Commerce Coll Informat Engn Tianjin Peoples R China Asia Univ Inst Innovat & Circular Econ Taichung Taiwan China Med Univ China Med Univ Hosp Taiwan Dept Med Res Taichung Taiwan Univ Kebangsaan Malaysia Grad Sch Business Bangi 43000 Selangor Malaysia Khon Kaen Univ Dept Ind Engn Dept Khon Kaen Thailand

This study addresses the high cost and high carbon emissions associated with the transportation sector. An environmental cold chain logistics distribution center location model has been designed by considering various constraints such as demand, time windows, and cost factors associated with cargo damage, refrigeration of cold chain cargo, and carbon emissions. An environmental cold chain logistics distribution center location model is designed for the raw materials of low-temperature storage and transportation in the semiconductor supply chain to achieve sustainable development by reducing carbon emissions. This study effectively reduces transportation costs and carbon emissions. To address the issues of slow convergence speed and susceptibility to local optima in the conventional whale optimization algorithm (WOA), a hybrid arithmetic whale optimization algorithm (HAWOA) is proposed by combining arithmetic optimization and the honey badger algorithm. Simulation analysis shows that the HAWOA is capable of obtaining the location scheme with the minimum distribution cost under the same number of iterations, which provides a substantial advantage compared to other algorithms.

关键词： Distribution center location Environmental cold chain Semiconductor supply chain whale optimization algorithm Hybrid arithmetic

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An improved multi-population whale optimization algorithm

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2022年第9期13卷 2447-2478页

作者： Navarro, Mario A. Oliva, Diego Ramos-Michel, Alfonso Zaldivar, Daniel Morales-Castaneda, Bernardo Perez-Cisneros, Marco Valdivia, Arturo Chen, Huiling Univ Guadalajara Div Tecnol Integrac Ciber Humana CUCEI Av Revoluc Guadalajara 1500 Jalisco Mexico Wenzhou Univ Dept Comp Sci Wenzhou 325035 Peoples R China

Clustering techniques and metaheuristic algorithms (MA) have demonstrated being efficient tools in their respective action fields. However, working together is an area marginally explored. One of the main disadvantages of MA is the lack of diversity in the solutions. Besides, most of them use only a single population to analyze the search space;this affects the capabilities to find the optimal solutions. This article proposes an approach called K-WOA that merges the benefits of two methods into a single algorithm. The K-means is a popular clustering technique based on centroids. Due to its simplicity and efficiency, combining it with a MA as the whale optimization algorithm (WOA) is ideal. This proposed K-WOA aims to increase the diversity of solutions in optimization problems by creating multiple groups of search agents operating cooperatively to explore the search space. To perform this task, the K-means is used in the initialization process to separate the population into different subgroups that the WOA independently evolves. In each sub-population, the best search agent is chosen to compare with the best agents of the other whale groups. By doing this, the algorithm can explore different regions of the search space simultaneously with more than one element. The K-WOA is proposed as an improved optimization algorithm that simultaneously searches for optimal solutions in multiple regions of the search space. The experimental results and comparisons with state-of-the-art approaches show that the proposed algorithm is competitive for solving complex optimization problems.

关键词： Metaheuristic algorithms Clustering K-means whale optimization algorithm

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Brain tumor diagnosis based on artificial neural network and a chaos whale optimization algorithm

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COMPUTATIONAL INTELLIGENCE 2020年第1期36卷 259-275页

作者： Gong, Shu Gao, Wei Abza, Francis Guangdong Univ Sci & Technol Dept Comp Sci Dongguan Peoples R China Yunnan Normal Univ Sch Informat Sci & Technol Kunming Yunnan Peoples R China Univ Ghana Dept Comp Sci Legon Ghana

Accurate and early detection of the brain tumor region has a great impact on the choice of treatment, its success rate, and the follow-up of the disease process over time. This study presents a new bioinspired technique for the early detection of the brain tumor area to improve the chance of completely healing. The study presents a multistep technique to detect the brain tumor area. Herein, after image preprocessing and image feature extraction, an artificial neural network is used to determine the tumor area in the image. The method is based on using an improved version of the whale optimization algorithm for optimal selection of the features and optimizing the artificial neural network weights for classification. Simulation results of the proposed method are applied to FLAIR, T1, and T2 datasets and are compared with different algorithms. Three performance indexes including correct detection rate, false acceptance rate, and false rejection rate are selected for the system performance analysis. Final results showed the superiority of the proposed method toward the other similar methods.

关键词： artificial neural network brain tumor feature classification tumor detection whale optimization algorithm

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Annual Rainfall Forecasting Using Hybrid Artificial Intelligence Model: Integration of Multilayer Perceptron with whale optimization algorithm

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WATER RESOURCES MANAGEMENT 2020年第2期34卷 733-746页

作者： Diop, Lamine Samadianfard, Saeed Bodian, Ansoumana Yaseen, Zaher Mundher Ghorbani, Mohammad Ali Salimi, Hana UGB UFR S2ATA St Louis Senegal Univ Tabriz Dept Water Engn Tabriz Iran UGB Lab Leidi Dynam Terr & Dev St Louis Senegal Ton Duc Thang Univ Sustainable Dev Civil Engn Res Grp Fac Civil Engn Ho Chi Minh City Vietnam

Rainfall, as one of the key components of hydrological cycle, plays an undeniable role for accurate modelling of other hydrological components. Therefore, a precise forecasting of annual rainfall is of the high importance. In this regard, several studies have been tried to predict annual rainfall of different climate zones using machine learning and soft computing algorithms. This study investigates the application of an innovative hybrid method, namely Multilayer Perceptron-whale optimization algorithm (MLP-WOA) to predict annual rainfall comparatively to the ordinary Multilayer Perceptron models (MLP). The models were developed by using 3-Input variables of annual rainfall at lag1, 2 and 3 corresponding to Pt-1, Pt-2 and P-t-3,P- respectively of two synoptic stations of Senegal (Fatick and Goudiry) in the time period of 1933-2013. 75% of the dataset were utilized for training and the other 25% for testing the studied models Accurateness of the mentioned models was examined using root mean squared error, correlation coefficient, and KlingGupta efficiency. Results showed that MLP-WOA3 and MLP3 using both Pt-1, Pt-2 and Pt-3 as inputs presented the most accurate forecasting in Fatick and Goudiry stations, respectively. In Fatick station, MLP-WOA3 decreased the RMSE value of MLP3 by 18.3% and increased the R and KGE values by 3.0% and 130%, respectively in testing period. But, in Goudiry station, MLP-WOA3 increased the RMSE value of MLP3 by 3.9% and increased the R and KGE values by 10.2% and 91% in testing period. Therefore, it can be realized that the MLP-WOA3 could not able to reduce the RMSE value of correspondent MLP model in Goudiry station. The conclusive results indicated that MLP-WOA slightly improved the accuracy of correspondent MLP models and may be recommended for annual rainfall forecasting.

关键词： Annual precipitation Multilayer perceptron model whale optimization algorithm Hybrid predictive model

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Parameter Reduction in Fuzzy Soft Set Based on whale optimization algorithm

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IEEE ACCESS 2020年 8卷 217268-217281页

作者： Kong, Zhi Zhao, Jie Yang, Qingfeng Ai, Jianwei Wang, Lifu Northeastern Univ Qinhuangdao Sch Control Engn Qinhuangdao 066004 Hebei Peoples R China

Parameter reduction is one of the hot topics in soft set theory. Deleting redundant parameters can reduce data and make decision effectively. In this article parameter reduction in fuzzy soft sets based on score decision-making method is discussed. The parameter reduction becomes more and more difficult with the number of parameters increasing. In order to find reduction quickly, a mathematical model of parameter reduction based on the score decision-making method is constructed, and the whale optimization algorithm is improved and tested. Finally, the reduction is obtained by using the improved whale optimization algorithm. The simulation results show that the method is effective.

关键词： Fuzzy soft set parameter reduction whale optimization algorithm score method

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A multistrategy hybrid adaptive whale optimization algorithm

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JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2022年第5期9卷 1952-1973页

作者： Li, Xinning Wu, Hu Yang, Qin Tan, Shuai Xue, Peng Yang, Xianhai Shandong Univ Technol Sch Mecharlcal Engn Zibo 255 Peoples R China Gachon Univ Grad Sch Seongnam Si 13120 South Korea Natl Engn Res Ctr Prod Equipment Dongying 257091 Peoples R China

To solve the problems of whale optimization algorithm (WOA) with slow convergence speed, low accuracy, and poor global search ability, a multistrategy hybrid adaptive whale optimization algorithm (MHWOA) was proposed. In this algorithm, the logistic-tent hybrid chaos algorithm was used to initialize the population, which could make the population distribution more random and uniform. The opposition-based learning strategy was adopted to expand the population of individuals and complete the population selection. To balance the exploitation phase and exploration phase, a dynamic parameter was constructed based on the sigmoid excitation function, and an active adaptive weight was added to adjust the global and local search, which accelerated the convergence speed also. The perturbation mechanism of the Student T-distribution was introduced with random perturbation to expand the search range and improve the global search ability of the algorithm. In total, 23 benchmark functions were selected to conduct convergence performance and optimization performance experiments of the proposed algorithm. The average value and standard deviation were determined as evaluation indexes. The MHWOA was compared with other improved WOA variants and advanced algorithms. The results showed that the proposed MHWOA had better iterative convergence and optimization performance than different algorithms on the optimization of unimodal functions, multimodal functions, and fixed dimension functions. Meanwhile, the MHWOA was applied to the optimal designs of pressure vessels and springs. The experimental results displayed that the MHWOA obtained better solutions than other meta-heuristic algorithms. This study has practical solid application value, which can be applied to solving various engineering problems.

关键词： whale optimization algorithm multistrategy hybrid adaptive optimization meta-heuristic algorithm

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Modified whale optimization algorithm Based on Tent Chaotic Mapping and Its Application in Structural optimization

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KSCE JOURNAL OF CIVIL ENGINEERING 2020年第12期24卷 3703-3713页

作者： Li, Yancang Han, Muxuan Guo, Qnglin Hebei Univ Engn Coll Civil Engn Handan 056038 Peoples R China

In this paper, a novel modified whale optimization algorithm based on Tent chaos map and tournament selection strategy (MWOA) was presented. The aim of the improved algorithm is to reduce the possibility of the standard whale algorithm falling into local optimal. During the initialization of the population, in order to increase population diversity and randomness, MWOA cites the Tent chaos map. In the optimization process, in order to improve the development ability of the standard algorithm, the tournament selection strategy was employed to improve the algorithm accuracy. Numerical simulation and example calculation results show that the improved algorithm is superior to the standard WOA algorithm. The improved method provides a new method for truss structure optimization.

关键词： whale optimization algorithm Tent chaotic mapping Selection strategy Standard function 0-1 Knapsack Problem Combinatorial optimization

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A modified whale optimization algorithm for large-scale global optimization problems

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EXPERT SYSTEMS WITH APPLICATIONS 2018年 114卷 563-577页

作者： Sun, Yongjun Wang, Xilu Chen, Yahuan Liu, Zujun Xidian Univ Sch Telecommun Engn State Key Lab Integrated Serv Networks 2 South Taibai Rd Xian 710071 Shaanxi Peoples R China

As a new and competitive population-based optimization algorithm, the whale optimization algorithm (WOA) outperforms some other biological-inspired algorithms from the perspective of simplicity and efficiency. However, WOA will get stuck into local optima and degrade accuracy for large-scale global optimization (LSGO) problems. To address the issue, a modified whale optimization algorithm (MWOA) is proposed for solving LSGO problems. In order to balance the exploration and exploitation abilities, a nonlinear dynamic strategy based on a cosine function for updating the control parameter is given. A Levy-flight strategy is adopted to make the algorithm jump out of local optima. Moreover, a quadratic interpolation method is applied to the leader of the population, which enhances the local exploitation ability and improves the solution accuracy. MWOA is tested over 25 well-known benchmark functions with dimensions ranging from 100 to 1000. The experimental results demonstrate the superior performance of MWOA on LSGO, in terms of solution accuracy, convergence speed, and stability compared with other state-of-the-art optimization algorithms. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Large-scale global optimization whale optimization algorithm Levy flight Self-adaptive strategy Quadratic interpolation

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