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Parkinson's disease and cleft lip and palate of pathological speech diagnosis using deep convolutional neural networks evolved by IPWOA

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APPLIED ACOUSTICS 2022年 199卷

作者： Yao, Dengfeng Chi, Wanle Khishe, Mohammad Beijing Union Univ Beijing Key Lab Informat Serv Engn Beijing 100101 Peoples R China Tsinghua Univ Sch Humanities Lab Computat Linguist Beijing 100084 Peoples R China Wenzhou Polytech Coll Artificial Intelligence Wenzhou 325035 Zhejiang Peoples R China Tech Univ Malaysia Malacca Fac Informat & Commun Technol Malacca 76100 Malacca State Malaysia Imam Khomeini Marine Sci Univ Dept Elect Engn Nowshahr Iran

A high-level abstract idea of speech is created in previously autonomous systems using unsupervised models. Given that these representations are typically acquired by input reconstruction, it cannot be said with assurance that they are resistant to cues unrelated to disease. Pathology diagnosis cannot usually be reliably performed using unsupervised representations. As a result, in this research, we use pathological voice recognition using deep convolutional neural networks (DCNNs). Even though DCNNs have many acknowledged benefits, selecting the best structure for them can be challenging. This work examines the use of the whale optimization algorithm (WOA) to automatically choose the best architecture for DCNNs in an effort to address this constraint. In order to achieve the goal, three canonical WOA-based innovations are suggested. First, a special encoding technique based on Internet Protocol Addresses (IPA) is created to make it easier to encode the DCNN layers with whale vectors. The development of variable-length DCNNs is then suggested using an enfeebled layer that has particular whale vector dimensions. The final step in the learning process involves splitting huge datasets into smaller ones and then randomly reviewing them. Pathological audio signals captured from patients are used to assess the performance of the proposed model. In this regard, five measures were used to conduct thorough research, including ROC and precision-recall curves, F1-Score, sensitivity, specificity, accuracy, and precision. Up to 95.77 percent of the two disordered speech signals are correctly classified by the suggested model, which outperforms the second-best algorithm, VLNSGA-II, by 1.02 percent in terms of accuracy. (C) 2022 Elsevier Ltd. All rights reserved.

关键词： Deep CNNs Pathological speech Parkinson's disease whale optimization algorithm Cleft lip and palate

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IRI: An intelligent resistivity inversion framework based on fuzzy wavelet neural network

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EXPERT SYSTEMS WITH APPLICATIONS 2022年 202卷

作者： Dong, Li Jiang, Feibo Li, Xiaolong Wu, Mingzhu Hunan Univ Technol & Business Changsha Social Lab Artificial Intelligence Changsha Peoples R China Hunan Normal Univ Hunan Prov Key Lab Intelligent Comp & Language In Changsha Peoples R China

To acquire high-quality resistivity inversion results, an intelligent resistivity inversion (IRI) framework based on a fuzzy wavelet neural network (FWNN) is proposed in this paper. In the IRI framework, first, the FWNN is applied to build an interpretable inversion model for analyzing the apparent resistivity data. Takagi-Sugeno-Kang (TSK) fuzzy model is introduced to FWNN to explain the rules of the inversion results, and the wavelet neural network (WNN) is applied to construct the consequent part for each fuzzy rule and enhance the high-order nonlinear fitting ability of TSK fuzzy model. Then, to enhance the generalization and robustness, an elastic gradient descent (EGD) method is designed, which is used to update the linear parameters of the FWNN. Next, a differential and adaptive whale optimization algorithm (DAWOA) is introduced to train the nonlinear parameters of the FWNN for avoiding local optimum. Moreover, in the proposed DAWOA, a differential foraging strategy and an adaptive predation strategy are introduced to improve global exploration and local exploitation. All these measures can improve the inversion accuracy and accelerate the training process of FWNN for ERI inversion. Several simulation results demonstrate the feasibility and applicability of the IRI framework.

关键词： Electrical resistivity imaging Fuzzy wavelet neural network whale optimization algorithm Elastic net

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A new multi-objective optimization algorithm combined with opposition-based learning

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 165卷

作者： Ewees, Ahmed A. Abd Elaziz, Mohamed Oliva, Diego Damietta Univ Dept Comp Dumyat Egypt Zagazig Univ Fac Sci Dept Math Zagazig Egypt Univ Oberta Catalunya IN3 Comp Sci Dept Castelldefels Spain Univ Guadalajara Dept Ciencias Computacionales CUCEI Av Revolut 1500 Guadalajara Jalisco Mexico

The optimization problems are divided into a single objective and multi-objective. Single objective optimization has only one objective function;whereas, multi-objective optimization has multiple objective functions that generate the Pareto set;therefore, solving a multi-objective problem is a challenging problem. This paper presents a new multi-objective optimization method (called MWDEO) based on improved whale optimization algorithm (WOA) by combining the differential evolution (DE) algorithm and the opposition-based learning (OBL). The MWDEO uses the WOA to perform a global exploration, whereas DE is used to exploit the search space;while the OBL is applied to improve the exploration and exploitation by generating the opposite values. The proposed algorithm is evaluated using 32 multi-objective test problems besides a set of benchmark problems of CEC'2017. The experimental results are compared with nine state-of-the-art multi-objective methods. The analysis of the results showed that the proposed MWDEO outperformed all other algorithms in most of the test problems which indicates that the proposed MWDEO is competitive and effective in solving different types of multi-objective problems.

关键词： Multi-objective optimization whale optimization algorithm Differential evolution Opposition-based learning

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Mass personalization-oriented integrated optimization of production task splitting and scheduling in a multi-stage flexible assembly shop

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COMPUTERS & INDUSTRIAL ENGINEERING 2021年 162卷

作者： Pang, Shibao Guo, Shunsheng Wang, Lei Li, Yibing Li, Xixing Liu, Zhengchao Wuhan Univ Technol Sch Mech & Elect Engn Wuhan 430070 Peoples R China Hubei Univ Technol Sch Mech Engn Hubei Key Lab Modern Mfg & Qual Engn Wuhan 430068 Peoples R China Jiangxi Univ Sci & Technol Sch Mech & Elect Engn Ganzhou 341000 Peoples R China

Aiming to produce personalized products at mass production efficiency, mass personalization has attained considerable interests in recent years. This paper considers an integrated task splitting and scheduling problem in the context of mass personalization, which converts customer requirements into a few production tasks at different levels and simultaneously optimizes the sizes as well as the scheduling sequence of the tasks. An in-tegrated optimization model for this problem is formulated to minimize makespan and two transfer strategies for the model are investigated. To solve the model, an improved whale optimization algorithm (IWOA) is proposed, where a first-whale search is designed to guide the search direction. Besides, an ocean current impact factor and differential evolution operators are combined in IWOA to enhance the search ability. Extensive computational experiments are performed to validate the proposed algorithm. Results show that compared with the comparison algorithms, IWOA can find the best solution and take relatively less time. Furthermore, statistical analysis proves that the results of multiple runs of IWOA are more concentrated, indicative of better stability of IWOA. Finally, a case study is presented in the paper to demonstrate the application of the proposed model and algorithm.

关键词： Mass personalization Task splitting Multi-stage assembly scheduling whale optimization algorithm

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The Arithmetic optimization algorithm

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COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2021年 376卷

作者： Abualigah, Laith Diabat, Ali Mirjalili, Seyedali Elaziz, Mohamed Abd Gandomi, Amir H. Amman Arab Univ Fac Comp Sci & Informat Amman 11953 Jordan New York Univ Abu Dhabi Div Engn Abu Dhabi 129188 U Arab Emirates NYU Tandon Sch Engn Dept Civil & Urban Engn Brooklyn NY 11201 USA Torrens Univ Australia Ctr Artificial Intelligence Res & Optimisat Brisbane Qld Australia Yonsei Univ YFL Yonsei Frontier Lab Seoul South Korea Zagazig Univ Dept Math Fac Sci Zagazig Egypt Wuhan Univ Technol Sch Comp Sci & Technol Wuhan Peoples R China Univ Technol Sydney Fac Engn & Informat Technol Ultimo NSW 2007 Australia

This work proposes a new meta-heuristic method called Arithmetic optimization algorithm (AOA) that utilizes the distribution behavior of the main arithmetic operators in mathematics including (Multiplication (M), Division (D), Subtraction (S), and Addition (A)). AOA is mathematically modeled and implemented to perform the optimization processes in a wide range of search spaces. The performance of AOA is checked on twenty-nine benchmark functions and several real-world engineering design problems to showcase its applicability. The analysis of performance, convergence behaviors, and the computational complexity of the proposed AOA have been evaluated by different scenarios. Experimental results show that the AOA provides very promising results in solving challenging optimization problems compared with eleven other well-known optimization algorithms. Source codes of AOA are publicly available at http://***/matlabcentral/fileexchange/8474 and https://***/projects. (C) 2020 The Author(s). Published by Elsevier B.V.

关键词： Arithmetic optimization algorithm AOA algorithm optimization Meta-heuristics Heuristic Artificial Intelligence Grey Wolf Optimizer whale optimization algorithm Genetic algorithm Particle Swarm optimization Benchmark Computational Intelligence

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Short-Term Traffic Flow Prediction Based On IWOA-WNN 33

Short-Term Traffic Flow Prediction Based On IWOA-WNN

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33rd Chinese Control and Decision Conference (CCDC)

作者： Yu, Qin Chen, Yuepeng Zhang, Qingyong Li, Li Ma, Wenqing Wuhan Univ Technol Sch Automat Wuhan 430070 Hubei Peoples R China

ISBN: (纸本)9781665440899

Aiming at the nonlinear and random characteristics of short-term traffic flow, an improved whale optimization algorithm (IWOA) is proposed to replace the gradient descent method to optimize the wavelet neural network (WNN) for short-term traffic flow prediction. Firstly, in view of the slow convergence speed and low convergence accuracy of the traditional whale optimization algorithm (WOA), a nonlinear convergence factor a is introduced to balance the global search and local search ability of the algorithm. At the same time, Kent chaotic mapping is used to increase population diversity and enhance the ability of jumping out to fall into local optimum. Secondly, aiming at the problem that the gradient descent method in the wavelet neural network is sensitive to the initial values of the weights and wavelet factors, and is easy to fall into local minimum values, the network weights of the wavelet neural network are optimized by improving the whale optimization algorithm. Finally, the wavelet threshold denoising algorithm is used to process the noise in the raw traffic flow sequence data, and the IWOA-WNN is used to test the short-term traffic flow data set after processing. The results show that the mean square error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of the model are 18.03, 2.82 and 13.13%, respectively. Experimental results show that the improved algorithm has higher accuracy than the raw algorithm, and the model can effectively improve the prediction accuracy of short-term traffic flow.

关键词： Short-Term Traffic Flow Prediction whale optimization algorithm Wavelet Neural Network Kent Chaos Mapping Wavelet Threshold Denoising

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Information sharing search boosted whale optimizer with Nelder-Mead simplex for parameter estimation of photovoltaic models

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ENERGY CONVERSION AND MANAGEMENT 2022年 270卷

作者： Peng, Lemin He, Caitou Heidari, Ali Asghar Zhang, Qian Chen, Huiling Liang, Guoxi Aljehane, Nojood O. Mansour, Romany F. Wenzhou Univ Dept Comp Sci & Artificial Intelligence Wenzhou 325035 Peoples R China Wenzhou Univ Technol Sch Data Sci & Artificial Intelligence Wenzhou 325035 Zhejiang Peoples R China Dept Informat Technol Wenzhou Polytech Wenzhou 325035 Peoples R China Univ Tabuk Fac Comp & Informat Technol Tabuk Saudi Arabia New Valley Univ Fac Sci Dept Math El Kharga 72511 Egypt

With the recent emphasis on new energy sources, solar photovoltaic cells have received widespread attention from scholars as a highly efficient and clean new energy source. Researchers model solar photovoltaic cells and analyze the values of various unknown parameters to improve their efficiency in converting solar energy into electric energy. To extract unknown parameters more accurately and efficiently, we propose a whale optimi-zation algorithm (WOA) driven by the information sharing search mechanism and the Nelder-Mead simplex (NMs) mechanism, which ISNMWOA. The WOA can perform a global search of the parameters of the photo-voltaic model to obtain a number of initial vectors of model parameters that satisfy the parameter range. The information sharing search strategy performs a further coarse local search on a number of feasible solutions generated. In contrast, the NMs can perform a further refined local search in the vicinity of the optimal pa-rameters to obtain the final vector of optimally performing model parameters. First, we tested ISNMWOA against six advanced algorithms in CEC 2014. We then used the ISNMWOA to evaluate the parameters of the solar cells and PV modules. The RMSE of ISNMWOA on single diode model (SDM), double diode model (DDM), three diode model (TDM), and photovoltaic module model (PV) are 9.8602E-04, 9.8248E-04, 9.8248E-04 and 2.4251E-03 respectively. Compared to the original WOA, the RMSE of ISNMWOA on SDM, DDM, TDM, and PV decreased by 30.01%, 24.24%, 50.98%, and 6.94%, respectively. Moreover, compared to the high-performing GOFPANM, ISNMWOA runs 298 times faster than GOFPANM with almost identical optimization results. We evaluated the performance of three commercial PV modules under different irradiance and temperature conditions using ISNMWOA to verify the reliability of the experiment. The experimental results show that the ISNMWOA out-performs similar algorithms currently available.

关键词： whale optimization algorithm optimization Swarm -intelligence Parameter estimation Photovoltaic models Solar energy

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Research on multi-modal autonomous diagnosis algorithm of COVID-19 based on whale optimized support vector machine and improved D-S evidence fusion

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COMPUTERS IN BIOLOGY AND MEDICINE 2022年 150卷 106181-106181页

作者： Wang, Guowei Guo, Shuli Han, Lina Song, Xiaowei Zhao, Yuanyuan Beijing Inst Technol Sch Automat State Key Lab Intelligent Control & Decis Complex Beijing 100081 Peoples R China Chinese Peoples Liberat Army Gen Hosp Med Ctr 2 Natl Clin Res Ctr Geriatr Dis Dept Cardiol Beijing Peoples R China

Aiming at the problem that the single CT image signal feature recognition method in the self-diagnosis of diseases cannot accurately and reliably classify COVID-19, and it is easily confused with suspected cases. The collected CT signals and experimental indexes are extracted to construct different feature vectors. The support vector machine is optimized by the improved whale algorithm for the preliminary diagnosis of COVID-19, and the basic probability distribution function of each evidence is calculated by the posterior probability modeling method. Then the similarity measure is introduced to optimize the basic probability distribution function. Finally, the multi-domain feature fusion prediction model is established by using the weighted D-S evidence theory. The experimental results show that the fusion of multi-domain feature information by whale optimized support vector machine and improved D-S evidence theory can effectively improve the accuracy and the precision of COVID-19 autonomous diagnosis. The method of replacing a single feature parameter with multi-modal indicators (CT, routine laboratory indexes, serum cytokines and chemokines) provides a more reliable signal source for the diagnosis model, which can effectively distinguish COVID-19 from the suspected cases.

关键词： COVID-19 Multi-modal information Support vector machine D-S evidence theory whale optimization algorithm

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Distribution parameter-determining method comparison for airborne wind energy potential assessment in the eastern coastal area of China

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SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS 2022年 52卷

作者： Wang, Jianzhou Li, Qiwei Ma, Xuejiao Lu, Haiyan Dongbei Univ Finance & Econ Sch Stat Dalian Peoples R China Dalian Univ Technol Sch Econ & Management Dalian Peoples R China Univ Technol Fac Engn & Informat Technol Sch Software Sydney NSW Australia

In recent years, airborne wind energy generation (AWEG) has increasingly been discussed as a better alternative to traditional wind turbine generation owing to less expensive raw materials, higher electricity capacity, and being more friendly to the surrounding environment. Thus, the planning of AWEG-based wind farms is necessary. To control the risk and ensure the future investment revenue to guarantee the ultimate success of this planning, airborne wind energy potential assessment (AWEPA) is essential;however, it has rarely been done so far. In this study, an AWEPA for the eastern coastal area of China was implemented. As wind speed distribution is an essential factor in AWEPA, to guarantee a reliable assessment, selecting a suitable distribution parameter determining method is critical. In this paper, two conventional numerical parameter-determining methods and three intelligent parameter-determining methods were compared, and the intelligent parameter-determining method embedding the whale optimization algorithm that showed outstanding performance in the comparison was finally selected to be used in the AWEPA. The assessment results show that developing AWEG in the study area is highly likely feasible, and the results also provide some other valuable information for planning.

关键词： Wind speed distribution whale optimization algorithm Flower pollination algorithm Genetic algorithm Parameter estimation

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Swarm-based optimization as stochastic training strategy for estimation of reference evapotranspiration using extreme learning machine

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AGRICULTURAL WATER MANAGEMENT 2021年 243卷

作者： Chia, Min Yan Huang, Yuk Feng Koo, Chai Hoon Univ Tunku Abdul Rahman Lee Kong Chian Fac Engn & Sci Dept Civil Engn Kampar Perak Malaysia

Reference evapotranspiration (ET0) is usually calculated as a pre-requisite to obtain the actual evapotranspiration (ET). Estimation of ET0 using extreme learning machine (ELM) is a new norm due to its excellent computation efficiency and its lower data dependency. However, the lack of stochastic tuning often results in the convergence to the local optima instead of the global optimum. This study attempts to address this issue by hybridizing the ELM with three swarm-based optimization algorithms, namely the particle swarm optimization (PSO), the moth-flame optimization (MFO) and the whale optimization algorithm (WOA) with different fitness functions. This study was conducted with three stations in Sabah and Sarawak (states in East Malaysia) as this region is to a very large extent covered with oil palm plantation. The water resources management is thus rather crucial for the area. The results showed that the WOA-ELM achieved a higher average rank score relative to the PSO-ELM and MFO-ELM, especially when the simple Taylor skill score was used as the fitness function. The application of different fitness functions did not show any significant effect. The reported standard deviations of the mean absolute error (MAE), root mean square error (RMSE) and coefficient of determination (R2) for the three variations of the hybridized ELM were close to 0;thus reflecting their similarity in accuracy. However, in terms of computational efficiency, the simple Taylor skill score exhibited better stability where it could effectively reduce the number of required hidden neurons when the input meteorological parameters were decreased. The WOA was suggested as the best optimization algorithm for this study. For the three stations, respectively it achieved an average MAE, RMSE and R-2 of 0.0007 to 0.1443, 0.0011 to 0.1927 and 1.0000 to 0.9486, from cases C1 to C6 with minimum time cost.

关键词： Extreme learning machine Reference evapotranspiration Particle swarm optimization Moth-flame optimization whale optimization algorithm Fitness function

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