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第34届中国控制与决策会议

作者： Huang Weichao Li Jiahao Liu Ding Faculty of automation and Information Engineering Xian University of Technology Shannxi Key Laboratory of Complex System Control and Intelligent Information Processing

A boundary inverse problem estimation method for heat conduction system based on a parameters adaptive PID algorithm is proposed in the *** method can solve the boundary heat flux estimation problem of onedimensional heat conduction model based on the inverse heat conduction *** reduce the error between the temperatures of the measurement points and the calculation values constantly by using feedback control of PID *** parameters of PID algorithm is optimized by whale optimization *** is enhances the rapidity and stability of the system and solves the problem that PID parameters are difficult to *** experimental results show that,the method that proposed in the paper can realize the inverse estimation of thermal boundary conditions accurately and quickly while ensuring the stability and convergence of the system.

关键词： Inverse heat conduction problem PID algorithm Finite difference method whale optimization algorithm

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Research on the efficient performance algorithm of fragmented blockchain for industrial Internet of Things applications 23

Research on the efficient performance algorithm of fragmente...

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Proceedings of the 2023 7th International Conference on Electronic Information Technology and Computer Engineering

作者： Yanbo Yin Jianming Zhao Tianyu Wang Weiyan Tong School of Chemical Process Automation Shenyang University of Technology China Key Laboratory of Networked Control Systems Chinese Academy of Sciences China

ISBN: (纸本)9798400708305

This article addresses the challenge of optimizing performance in a fragmentation blockchain system for industrial Internet of things (IIOT) applications. It introduces an enhanced version of the traditional whale optimization algorithm (WOA) known as the Chaotic whale optimization algorithm. This enhanced algorithm is designed to optimize the parameters of the fragmentation blockchain system with the goal of maximizing real-time memory utilization in the fragmentation nodes while minimizing block consensus time. The proposed method identifies optimal blockchain system parameters within diverse network environments, taking into account factors such as node computing power and inter-node transmission rates. To mitigate the issue of local optima inherent in traditional algorithms, the Chaotic whale optimization algorithm transforms the optimization problem in a more intuitive manner. This algorithm strikes a fine balance between diversity and convergence. The effectiveness of this method is assessed through simulation experiments, with results indicating its superior performance compared to alternative approaches. Overall, the approach presented in this article significantly reduces consensus time within the fragmentation blockchain system and enhances real-time memory utilization in the fragmented blocks.

关键词： consensus time industrial Internet of Things real-time utilization of node memory whale optimization algorithm

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A WOA-SVDD based Axial Compressor Instability Detection

A WOA-SVDD based Axial Compressor Instability Detection

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第32届中国过程控制会议(CPCC2021)

作者： Xinyuan Dong Peng Lin Machine Learning and I-health International Cooperation Base of Zhejiang Province and with the Artificial Intelligence InstituteHangzhou Dianzi University

As one of the most critical module in aircraft engine,axial compressor instability limits the whole performance mostly,so the detection and control of it is particularly *** that the compressor instability is abnormal,an established one-class classifier called support vector data description(SVDD) is chosen for anomaly detection due to few observation data of compressor *** whale optimization(WOA) algorithm is taken to optimize the parameters of SVDD,and a WOASVDD model is proposed for the detection of axial compressor *** validity of the proposed WOA-SVDD model is proved by simulation on the data from a low speed axial compressor.

关键词： anomaly detection Support vector data description whale optimization algorithm Compressor instability

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Classifying Heart Disease in Medical Data Using Deep Learning Methods

Classifying Heart Disease in Medical Data Using Deep Learnin...

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作者： T.Velmurugan U.Latha PG and Research Department of Computer Science D.G.Vaishnav College

Recent days, heart ailments assume a fundamental role in the world. The physician gives different name for heart disease, for example, cardiovascular failure, heart failure and so on. Among the automated techniques to discover the coronary illness, this research work uses Named Entity Recognition(NER) algorithm to discover the equivalent words for the coronary illness content to mine the significance in clinical reports and different applications. The Heart sickness text information given by the physician is taken for the preprocessing and changes the text information to the ideal meaning, at that point the resultant text data taken as input for the prediction of heart disease. This experimental work utilizes the NER to discover the equivalent words of the coronary illness text data and currently uses the two strategies namely Optimal Deep Learning and whale optimization which are consolidated and proposed another strategy Optimal Deep Neural Network(ODNN) for predicting the illness. For the prediction, weights and ranges of the patient affected information by means of chosen attributes are picked for the experiment. The outcome is then characterized with the Deep Neural Network and Artificial Neural Network to discover the accuracy of the algorithms. The performance of the ODNN is assessed by means for classification methods, for example, precision, recall and f-measure values.

关键词： Named Entity Recognition algorithm Neural Network Methods whale optimization algorithm F-Measure Recall Precision

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A hybrid whale algorithm and pattern search technique for optimal power flow problem

A hybrid whale algorithm and pattern search technique for op...

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International Conference on Modelling, Identification and Control

作者： B. BENTOUATI L. CHAIB S. CHETTIH LACoSERE laboratory Amar Telidji University Laghouat

ISBN: (纸本)9781509015948

This paper presents a new power system planning strategy by combining whale optimization algorithm (WOA) with pattern search algorithm (PS). The proposed approach has been carried out on the IEEE 30-bus test system considering several objective functions, such as generating fuel cost, voltage profile improvement, minimization of total power losses and emission reduction are also considered. The obtained results are compared with recently published metaheuristic algorithms. Simulation results clearly reveal the effectiveness and the rapidity of the proposed algorithm for solving the OPF problem.

关键词： whale optimization algorithm Pattern search Optimal power flow optimization Metaheuristics

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Closed loop fractional order drug delivery control scheme for chemotherapy

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RESULTS IN CONTROL AND optimization 2022年 6卷

作者： Pachauri, Nikhil Yadav, Drishti Sharma, Tarun Kumar Verma, Om Prakash Ahn, Chang Wook Gwangju Inst Sci & Technol AI Grad Sch 123 Cheomdangwagi Ro Gwangju 61005 South Korea Tech Univ Wien Fac Informat Inst Comp Engn A-1040 Vienna Austria Shobhit Univ Gangoh Dept Comp Sci & Engn Saharanpur 247341 India Dr BR Ambedkar Natl Inst Technol Dept Instrumentat & Control Engn Jalandhar 144011 India

Chemotherapy is one of the most extensively utilized cancer treatment strategies worldwide. It is intended to eliminate fast -developing cancer cells in a patient's body. The amount of chemotherapeutic drug that must be administered precisely into a patient's body determines the efficacy of the treatment and governs the patient survival during chemotherapy. Therefore, controlling the chemotherapeutic drug dose delivered to the patient is essential. This research aims to propose a two -degree -of -freedom fractional order proportional-integral-derivative (2FOPID) controller with a set point filter for implementing an automatic drug delivery control scheme during chemotherapy. The whale optimization algorithm (WOA) is used to tune the parameters of the 2FOPID controller, resulting in a WOA-tuned 2FOPID controller (W2FOPID). The performance of the proposed W2FOPID is compared with the Integral-Proportional-Derivative (IPD), Internal Model Control (IMC), and Fractional Order IMC (FOIMC) schemes. The experimental results demonstrate that the proposed W2FOPID controller is effective, accurate, and robust for drug concentration control during chemotherapy. W2FOPID outperforms IPD, IMC, and FOIMC schemes in terms of Integral Absolute Error by 79.9%, 25.3%, and 23.36%, respectively. In addition, W2FOPID exhibits excellent set -point tracking, noise suppression and uncertainty handling capabilities.

关键词： FOPID Chemotherapy Drug concentration FOIMC IPD whale optimization algorithm

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Multi-objective evolutionary method for multi-area dynamic emission/economic dispatch considering energy storage and renewable energy units

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Heliyon 2024年第18期10卷 e37476页

作者： Shahri, Hassan Yaghubi Hosseini, Seyed Ali Pourhossein, Javad Department of Electrical Engineering Islamic Azad University Gonabad Branch Gonabad Iran

Reducing thermal unit operating costs and emissions is the goal of the multi-objective issue known as multi-area economic/emission dispatch (MAEED) in smart grids. Using renewable energy (RE) have significantly lowered greenhouse gas emissions and ensured the sustainability of the environment. With regard to constraints such as prohibited operating zones (POZs), valve point effect (VPE), transmission losses in the network, ramp restrictions, tie-line capacity, this study aims to minimize operating costs and emission objectives by solving the multi-area dynamic economic/emission dispatch (MADEED) problem in the presence of RE units and energy storage (ES) systems. The conventional economic dispatch (ED) optimization approach has the following shortcomings: It is only designed to solve the single-objective optimization problem with a cost objective, in addition, it also does not have high calculation accuracy and speed. Therefore, to address this multi-objective MADEED problem with non-linear constraints, this paper introduces hybrid particle swarm optimization (PSO)-whale optimization algorithms (WOA). The reason for combining two algorithms is to use the advantages of both algorithms in solving the desired optimization problem. The introduced method is tested in two separate scenarios on a test network of 10 generators. Using the suggested hybrid methodology in this study, the MADED and MADEED problems are resolved and contrasted with other evolutionary techniques, such as original WOA, and PSO methods. Examining the results of the proposed method shows the efficiency and better performance of the proposed method compared to other methods. Finally, the results obtained by simulations indicate that integrating the necessary system restrictions gives the system legitimacy and produces dependable output. With regard to the results obtained from the introduced approach, the value of the overall cost function has clearly decreased by about 3 % compared to other methods.

关键词： Energy storage systems Multi-area dynamic economic/emission dispatch Particle swarm algorithm Renewable energy units whale optimization algorithm

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A mixed-integer non-linear no-wait open-shop scheduling model for minimizing makespan and total tardiness in manufacturing

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Decision Analytics Journal 2024年 10卷

作者： Mirmozaffari, Mirpouya Hejazi, Seyed Mojtaba Karamizadeh, Narges Montazeri, Arsalan Department of Industrial Engineering Dalhousie University 5269 Morris Street Halifax B3H 4R2 NS Canada Department of Data Science Rome Business School Rome Italy Department of Civil and Resource Engineering Dalhousie University 1360 Barrington Street Halifax B3H 4R2 NS Canada Northeastern University Department of project management First Canadian Place 100 King St W Toronto M5X 1E2 ON Canada

Open shop scheduling problems (OSSP) are highly significant in engineering and industry, involving critical scheduling challenges. The job type determines the duration required for material transfer between machines and remains unaffected by distance, eliminating any waiting time for transfers. This study also investigates machine-to-machine transfer durations, influenced by job types and independent of distance. The approach considers a transportation system allowing an unrestricted quantity of transportation machines, ensuring no delay is required for open shop scheduling during transfers. These specific scheduling problems are described as no-wait open shop scheduling problems (NWOSP). A mathematical model called mixed integer nonlinear programming models (MINLP) is utilized, employing the GAMS BONMIN solver for small and medium-scale problems to address smaller-scale instances of these problems. Three metaheuristic algorithms of Grey Wolf Optimizer (GWO), whale optimization algorithm (WOA), and Ant Lion Optimizer (ALO) are compared to resolve large-scale problems by considering a sorting function assisted by MATLAB to change the continuous metaheuristics environment to a permutation scheduling problem considering 250 iterations. Since metaheuristics are a theoretically powerful optimizer tool designed to expedite the resolution of intricate single-objective nonlinear problems and locate the most advantageous solution, reference point programming with Euclidean distances (RPPED) is applied as the powerful tool for expediting the nonlinearity conditions and converting the bi-objective mixed integer linear programming (BOMILP) to suggested single objective MINLP. © 2024 The Author(s)

关键词： Ant Lion Optimizer Grey Wolf Optimizer Makespan Mixed integer nonlinear programming No-wait open shop scheduling problems Reference point programming with Euclidean distances Total tardiness Transportation time whale optimization algorithm

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Hybrid deep learning and isogeometric analysis for bearing capacity assessment of sand over clay

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Journal of Rock Mechanics and Geotechnical Engineering 2024年

作者： Toan Nguyen-Minh Tram Bui-Ngoc Jim Shiau Tan Nguyen Trung Nguyen-Thoi Faculty of Civil Engineering Ton Duc Thang University Ho Chi Minh City 700000 Vietnam Laboratory for Computational Mechanics Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City 700000 Vietnam Faculty of Mechanical - Electrical and Computer Engineering School of Technology Van Lang University Ho Chi Minh City 700000 Vietnam School of Engineering University of Southern Queensland Toowoomba QLD 4350 Australia Smart Computing in Civil Engineering Research Group Ton Duc Thang University Ho Chi Minh City 700000 Vietnam Laboratory for Applied and Industrial Mathematics Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City 700000 Vietnam

In this paper, Isogeometric analysis (IGA) is effectively integrated with machine learning (ML) to investigate the bearing capacity of strip footings in layered soil profiles, with a focus on a sand-over-clay configuration. The study begins with the generation of a comprehensive dataset of 10,000 samples from IGA upper bound (UB) limit analyses, facilitating an in-depth examination of various material and geometric conditions. A hybrid deep neural network, specifically the whale optimization algorithm-Deep Neural Network (WOA-DNN), is then employed to utilize these 10,000 outputs for precise bearing capacity predictions. Notably, the WOA-DNN model outperforms conventional ML techniques, offering a robust and accurate prediction tool. This innovative approach explores a broad range of design parameters, including sand layer depth, load-to-soil unit weight ratio, internal friction angle, cohesion, and footing roughness. A detailed analysis of the dataset reveals the significant influence of these parameters on bearing capacity, providing valuable insights for practical foundation design. This research demonstrates the usefulness of data-driven techniques in optimizing the design of shallow foundations within layered soil profiles, marking a significant stride in geotechnical engineering advancements.

关键词： UB limit analysis Isogeometric analysis (IGA) hybrid deep neural network whale optimization algorithm

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