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Bearing fault diagnosis using a whale optimization algorithm-optimized orthogonal matching pursuit with a combined time-frequency atom dictionary

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2018年 107卷 29-42页

作者： Zhang, Xin Liu, Zhiwen Miao, Qiang Wang, Lei Sichuan Univ Sch Aeronaut & Astronaut Chengdu 610065 Sichuan Peoples R China

Condition monitoring and fault diagnosis of rolling element bearings are significant to guarantee the reliability and functionality of a mechanical system, production efficiency, and plant safety. However, this is almost invariably a formidable challenge because the fault features are often buried by strong background noises and other unstable interference components. To satisfactorily extract the bearing fault features, a whale optimization algorithm (WOA)-optimized orthogonal matching pursuit (OMP) with a combined time frequency atom dictionary is proposed in this paper. Firstly, a combined time-frequency atom dictionary whose atom is a combination of Fourier dictionary atom and impact time-frequency dictionary atom is designed according to the properties of bearing fault vibration signal. Furthermore, to improve the efficiency and accuracy of signal sparse representation, the WOA is introduced into the OMP algorithm to optimize the atom parameters for best approximating the original signal with the dictionary atoms. The proposed method is validated through analyzing the bearing fault simulation signal and the real vibration signals collected from an experimental bearing and a wheelset bearing of highspeed trains. The comparisons with the respect to the state of the art in the field are illustrated in detail, which highlight the advantages of the proposed method. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Bearing fault diagnosis Sparse decomposition Orthogonal matching pursuit Combined time-frequency atom dictionary whale optimization algorithm

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A novel product shape design method integrating Kansei engineering and whale optimization algorithm

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ADVANCED ENGINEERING INFORMATICS 2024年 62卷

作者： Zhao, Xiang Sharudin, Sharul Azim Lv, Han-Lu City Univ Malaysia Kuala Lumpur 46100 Malaysia West Univ Appl Sci Tengchong 679100 Peoples R China Daejin Univ Pochon 11159 South Korea

The focus of consumer desire transitions from product functionality to emotional resonance in experience economy era, wherein emotional needs of users increasingly become a critical factor in product design. However, traditional approaches to product shape design often rely heavily on the designer's intuition and experience, sometimes neglecting to incorporate emotional and humanistic elements into the product's shape, thus resulting in inconsistencies in design results and quality. To address this challenge, this study introduces a novel method for emotionally driven product shape design that integrates Kansei engineering and the whale optimization algorithm (WOA). This approach aims to fulfill consumer emotional demands related to product form and enhance overall user satisfaction. Firstly, the process utilized Python web crawlers to collect online product review texts and product images from e-commerce platforms. Next, Latent Dirichlet Allocation (LDA) and Analytic Hierarchy Process (AHP) were employed to extract representative emotional vocabularies, while representative samples were defined and deconstructed through clustering and morphological analysis. Then, semantic Differential (SD) questionnaires were distributed to collect consumer evaluations of product shape imagery, leading to the development of a user emotional prediction model for product shape. Then, WOA was introduced to optimize the performance of Back Propagation Neural Network (BPNN) and Support Vector Regression (SVR) models. Finally, Particle Swarm optimization (PSO) and Seagull optimization algorithm (SOA) were employed to improve the prediction model, and the effect of these models was compared by the error method. This analysis explored the accuracy of these non-linear models in order to identify the optimal model for application in product form design cases. The scientific validity and effectiveness of this method were demonstrated utilizing whiskey bottle shape design as a case study. The

关键词： Product Shape Design Kansei Engineering Latent Dirichlet Allocation whale optimization algorithm Whiskey Bottle Shape

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A novel hybrid whale optimization algorithm with flower pollination algorithm for feature selection: Case study Email spam detection

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COMPUTATIONAL INTELLIGENCE 2021年第1期37卷 176-209页

作者： Mohammadzadeh, Hekmat Gharehchopogh, Farhad Soleimanian Islamic Azad Univ Urmia Branch Dept Comp Engn Orumiyeh Iran

Feature selection (FS) in data mining is one of the most challenging and most important activities in pattern recognition. In this article, a new hybrid model of whale optimization algorithm (WOA) and flower pollination algorithm (FPA) is presented for the problem of FS based on the concept of opposition-based learning (OBL) which name is HWOAFPA. The procedure is that the WOA is run first and at the same time during the run, the WOA population is changed by the OBL. And, to increase the accuracy and speed of convergence, it is used as the initial population of FPA. To evaluate the performance of the proposed method, experiments were carried out in two steps. The experiments were performed on 10 datasets from the UCI data repository and Email spam detection datasets. The results obtained from the first step showed that the proposed method was more successful in terms of the average size of selection and classification accuracy than other basic metaheuristic algorithms. In addition, the results from the second step showed that the proposed method which was a run on the Email spam dataset performed much more accurately than other similar algorithms in terms of accuracy of Email spam detection.

关键词： email spam detection feature selection flower pollination algorithm opposition-based learning optimization whale optimization algorithm

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Multiobjective feature selection for key quality characteristic identification in production processes using a nondominated-sorting-based whale optimization algorithm

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COMPUTERS & INDUSTRIAL ENGINEERING 2020年 149卷 106852-106852页

作者： Li, An-Da He, Zhen Tianjin Univ Commerce Sch Management Tianjin 300134 Peoples R China Tianjin Univ Coll Management & Econ Tianjin 300072 Peoples R China

Identifying key quality characteristics (QCs) in production processes is essential for product quality control and improvement. This paper proposes a multiobjective wrapper-based feature selection (FS) method for key QC (KQC) identification on unbalanced production data using a novel modified nondominated-sorting-based whale optimization algorithm (MNSWOA) and the ideal point method (IPM). In the proposed approach, the FS problem is defined as maximizing the geometric mean (GM) measure and minimizing the feature (QC) subset size. To solve the defined FS problem, MNSWOA is adopted first to find a set of candidate solutions (feature subsets), and then IPM is adopted to select the final solution. In MNSWOA, a modified fast nondominated sorting approach is proposed to adapt the single objective whale optimization algorithm to the multiobjective scenario. Moreover, a uniform reference solution selection strategy and the mutation operations are embedded in MNSWOA to improve its search performance. Experimental results on four unbalanced production datasets show that the proposed FS method performs effectively and efficiently for KQC identification. Further comparisons show that MNSWOA obtains better search performance than benchmark multiobjective optimization methods, including a modified NSGA-II, SPEA2, MOEA/D, NSPSO and CMDPSO.

关键词： Feature selection whale optimization algorithm Multiobjective optimization Classification Unbalanced data Quality improvement

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Enhanced whale optimization algorithm-based modeling and simulation analysis for industrial system parameter identification

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JOURNAL OF SUPERCOMPUTING 2023年第13期79卷 14489-14544页

作者： Braik, Malik Awadallah, Mohammed Al-Betar, Mohammed Azmi Al-Hiary, Heba Al Balqa Appl Univ Dept Comp Sci Salt Jordan Al Aqsa Univ Dept Comp Sci Gaza Palestine Ajman Univ Artificial Intelligence Res Ctr AIRC Ajman U Arab Emirates Al Balqa Appl Univ Dept Informat Technol Salt Jordan Al Balqa Appl Univ Dept Comp Informat Syst Salt Jordan

Parameter identification for complex systems of nonlinear nature is challenging due to the complicated process structure and large number of parameters with different identifiability. The scope of this work is to develop a model-based parameter identification method for a nonlinear industrial winding system. The proposed parameter identification method consists of two key steps: First, enhanced whale optimization algorithm (EWOA) was proposed to alleviate the issues of low search performance and premature convergence of WOA, for which the following enhancements were made to WOA: (1) improvements to the bubble-net strategy of its mathematical model, (2) amendments to the humpback whales' movements in the direction of the best whales, and (3) imitation of the schooling behavior of humpback whales when chasing prey. Second, EWOA was acted as a training method for artificial neural networks (ANNs)-type multilayer perceptron (MLP), a method referred to as EWOA-MLP. In this, EWOA was applied to train ANNs in order to mitigate the main difficulties of ANNs due to their nonlinear nature and unknown optimal set of control parameters (i.e., weights and biases). The performance of the proposed EWOA-MLP was assessed in modeling the subsystems of the winding process under study and in solving fifteen classification datasets. The effectiveness of EWOA-MLP in both modeling and classification studies was judged by several pertinent assessment metrics. Results of comparison of the proposed EWOA-MLP with other promising methods firmly confirm the promising performance of EWOA-MLP for both local optima avoidance and convergence rate, proving its value and superiority. Moreover, EWOA-MLP was able to outperform other algorithms in modeling the winding process and solving many classification problems.

关键词： whale optimization algorithm Artificial neural network Industrial winding process Multilayer Perceptron Classification

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A hybrid style transfer with whale optimization algorithm model for textual adversarial attack

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NEURAL COMPUTING & APPLICATIONS 2024年第8期36卷 4263-4280页

作者： Kang, Yan Zhao, Jianjun Yang, Xuekun Fan, Baochen Xie, Wentao Yunnan Univ Natl Pilot Sch Software Key Lab Software Engn Yunnan Prov Kunming 650500 Yunnan Peoples R China Broadvis Engn Consultants Co Ltd Natl Engn Lab Surface Transportat Weather Impacts Kunming Peoples R China

Deep learning has been widely used in various research fields. However, researchers have discovered that deep learning models are vulnerable to adversarial attacks. Existing word-level attacks can be seen as a combinatorial optimization problem to effectively conduct textual adversarial attacks, but inappropriate search spaces and search methods may affect attack effectiveness. Sentence-level attacks are successfully used in the field of reading comprehension, but the generated examples sometimes lead to semantic deviation. To address these issues, we propose a hybrid textual adversarial attack method that effectively enhances the performance of textual adversarial attacks. To the best of our knowledge, we are the first to conduct textual adversarial attacks by hybridizing whale optimization algorithm (WOA) with style transfer from multiple sentence and word levels. The WOA is improved by incorporating data characteristics and the Metropolis criterion to escape from local optima and by leveraging the mutation operator to increase population diversity. The improved WOA and style transfer algorithm are fused in a parallel and vertical way. Style transfer can increase population diversity and expand the search space, usually without destroying the semantics and syntax of sentences. The parallel combination improves attack performance by attacking from both word-level and sentence-level perspectives. As a black-box attack model, our method can attack without knowing the internal structure of the model. Compared with the state-of-the-art method, our framework can improve the attack success rate by 6.8%. Additionally, further experiments on grammatical error increase rates, semantic consistency, and transferability demonstrate that our model has excellent performance in many respects.

关键词： Natural language processing Textual adversarial attacks Black box attacks Artificial intelligence security whale optimization algorithm Text style transfer

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An enhanced whale optimization algorithm for DNA storage encoding

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2022年第12期19卷 14142-14172页

作者： Wang, Sijie Zhou, Shihua Yan, Weiqi Dalian Univ Sch Software Engn Key Lab Adv Design & Intelligent Comp Minist Educ Dalian Peoples R China Auckland Univ Technol Sch Engn Comp & Math Sci Auckland 1010 New Zealand

Metaheuristic algorithms have the drawback that local optimal solutions are prone to precocious convergence. In order to overcome the disadvantages of the whale optimization algorithm, we propose an improved selective opposition whale optimization algorithm (ISOWOA) in this paper. Firstly, the enhanced quasi-opposition learning (EQOBL) is applied to selectively update the position of the predator, calculate the fitness of the population before and after, and retain optimal individuals as the food source position;Secondly, an improved time-varying update strategy for inertia weight predator position is proposed, and the position update of the food source is completed by this strategy. The performance of the algorithm is analyzed by 23 benchmark functions of CEC 2005 and 15 benchmark functions of CEC 2015 in various dimensions. The superior results are further shown by Wilcoxon's rank sum test and Friedman's nonparametric rank test. Finally, its applicability is demonstrated through applications to the field of biological computing. In this paper, our aim is to achieve access to DNA files and designs high-quantity DNA code sets by ISOWOA. The experimental results show that the lower bounds of the multi-constraint storage coding sets implemented in this paper equals or surpasses that of previous optimal constructions. The data show that the amount of the DNA storage cods filtered by ISOWOA increased 2-18%, which demonstrates the algorithm's reliability in practical optimization tasks.

关键词： whale optimization algorithm enhanced quasi-opposition learning time-varying inertia weight DNA storage set

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Group-based whale optimization algorithm

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SOFT COMPUTING 2020年第5期24卷 3647-3673页

作者： Hemasian-Etefagh, Farinaz Safi-Esfahani, Faramarz Islamic Azad Univ Najafabad Branch Fac Comp Engn Najafabad Iran Islamic Azad Univ Najafabad Branch Big Data Res Ctr Najafabad Iran

Meta-heuristic algorithms are divided into two categories: biological and non-biological. Biological algorithms are divided into evolutionary and swarm-based intelligence, where the latter is divided into imitation based and sign based. The whale algorithm is a meta-heuristic biological swarm-based intelligence algorithm (based on imitation). This algorithm suffers from the early convergence problem which means the population convergences early to an unfavorable optimum point. Usually, the early convergence occurs because of the weakness in exploration capability (global search). In this study, an optimized version of the whale algorithm is proposed that introduces a new idea in grouping of whales (called GWOA) to overcome the early convergence problem. The proposed whale optimization algorithm is compared with the standard whale algorithm (WOA), CWOA improved whale algorithm, particle swarm optimization, and BAT algorithms applying CEC2017 functions. The results of the experiments show that the proposed method applying Friedman's test on 30 standard benchmark functions has a better performance than the other baseline algorithms.

关键词： Biological Meta-heuristic algorithm whale optimization algorithm

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An improved whale optimization algorithm for forecasting water resources demand

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APPLIED SOFT COMPUTING 2020年 86卷

作者： Guo, Wenyan Liu, Ting Dai, Fang Xu, Peng Xian Univ Technol Sch Sci Xian 710054 Shaanxi Peoples R China

Water demand forecasting can promote the rational use of water resources and alleviate the pressure on water demand. By analyzing the use of water resources, this paper establishes three models of water demand forecasting, logarithmic model, linear and exponential combination model and linear, exponential and logarithmic hybrid models. In order to accurately estimate the demand for water resources, an improved whale optimization algorithm based on social learning and wavelet mutation strategy is proposed. The new algorithm designs a new linear incremental probability, which increases the possibility of global search of the algorithm. Based on the social learning principle, the social ranking and social influence are used to construct the social network for the individual, and the adaptive neighborhood learning strategy based on the network relationship is established to achieve the exchange and sharing of information between groups. The Morlet wavelet mutation mechanism is integrated to realize the dynamic adjustment of the mutation space, which enhances the ability of the algorithm to escape from local optimization. The latest CEC2017 benchmark functions confirms the superiority of the proposed algorithm. The water consumption from 2004 to 2016 in Shaanxi Province of China is used for the experiment. The results show that the performance of the proposed algorithm for solving the three water resources forecasting model is better in comparison to other algorithms. The prediction accuracy is as high as 99.68%, which verified the validity of the model and the practicality of the proposed algorithm. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Water demand forecasting whale optimization algorithm Social learning Social network Wavelet mutation CEC2017 benchmark functions

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Method using singular value decomposition and whale optimization algorithm to quantitatively detect multiple damages in turbine blades

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STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL 2024年第2期23卷 1025-1036页

作者： Jiang, Hu Jiang, Yongying Xiang, Jiawei Wenzhou Univ Coll Mech & Elect Engn Wenzhou Zhejiang Peoples R China Wenzhou Univ Coll Mech & Elect Engn Wenzhou 325035 Zhejiang Peoples R China

Renewable energy has increased in recent years with a consequential increase in equipment maintenance. Maintenance costs can be reduced by structural health monitoring techniques especially for wind turbine (WT) blade damages. However, the majority are not suitable for on-line measurements and quantitative detections. A quantitative damage detection method is developed to identify multiple damages in a WT blade under in-service operation conditions. Firstly, singular value decomposition is applied to reveal singular information in the operating deflection shape (ODS), which can be treated as damage locations. Secondly, whale optimization algorithm is utilized for a damage severity decision about the natural frequency database between damage severities and natural frequencies, which are constructed by finite element method (FEM) simulations on the detected damage locations in the WT blade. The procedure is applied to FEM numerical simulations of a single WT blade with two and three damages. By adding a certain noise to the simulation dataset, the robustness of the present method is validated. Furthermore, the laser scanning vibrometer is employed to test the ODS as well as natural frequencies of WT blades to testify the performance of the multiple damage detection method. Results show that the present method is effective for the detection of multi-damage in WT blades with a certain noise robustness.

关键词： Wind turbine blade operating deflection shape singular value decomposition whale optimization algorithm multi-damage detection

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