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Damage Detection in Members and Connections of Plane Frame with Flexible Connections Using Residual Force Method and whale optimization algorithm

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IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF CIVIL ENGINEERING 2024年第4期48卷 1917-1931页

作者： Hamdy, Osman Nawar, Mahmoud T. Zagazig Higher Inst Engn & Technol Dept Civil Engn Zagazig Egypt Prince Sultan Univ Coll Engn Engn Management Dept Riyadh Saudi Arabia Zagazig Univ Struct Engn Dept Zagazig 44519 Egypt

This paper proposes a quantification and location damage detection model for plane frames with flexible connections considering simultaneous damage in members and connections. A two-phase method is produced to decrease the computational efforts considerably. The first phase presents proposed damage indicators depending on the residual force vector concept to obtain the expected damaged members and connections separately. The second phase considers damage quantification as a variable into the whale optimization algorithm (WOA) to obtain the optimum damage quantification value of the expected damaged members and connections attained in the first phase. WOA is a recent promising algorithm that has shown excellence in optimizing structural problems. Moreover, the biogeography-based optimization (BBO) is used in the second phase to compare the WOA and BBO algorithms. As it is obvious, the first phase diminishes the search space in the second phase, which in turn leads to a substantial reduction in computational efforts. The model is applied on three plane frame examples with flexible beam-to-column connections considering different damage scenarios. Results have proved the proficiency of the proposed method to accurately detect the quantification and location of damage with minimal computational efforts, and the superiority of WOA in comparison to BBO.

关键词： Damage detection whale optimization algorithm Plane frame Flexible connection Residual force vector

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Optimizing scientific workflow scheduling in cloud computing: a multi-level approach using whale optimization algorithm

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Journal of Engineering and Applied Science 2024年第1期71卷 175页

作者： Zhang, Xiaowen School of Computer and Information Engineering Henan University of Economics and Law Zhengzhou450000 China

Cloud computing has evolved into an indispensable tool for facilitating scientific research due to its ability to efficiently distribute and process workloads in a virtual environment. Scientific tasks that involve complicated task dependencies and user-defined constraints related to quality of service (QoS) and time constraints require the efficient use of cloud resources. Planning these scientific workflow tasks represents an NP-complete problem, prompting researchers to explore various solutions, including conventional planners and evolutionary optimization algorithms. In this study, we present a novel, multistage algorithm specifically designed to schedule scientific workflows in cloud computing contexts. This approach addresses the challenges of efficiently mapping complex workflows onto distributed cloud resources while considering factors like resource heterogeneity, dynamic workloads, and stringent performance requirements. The algorithm uses the whale optimization algorithm (WOA) with a two-phase approach to shorten execution time, minimize financial costs, and effectively maintain load balancing. © The Author(s) 2024.

关键词： Cloud computing QoS whale optimization algorithm Workflow scheduling

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whale optimization algorithm Based on Artificial Fish Swarm algorithm 8th

Whale Optimization Algorithm Based on Artificial Fish Swarm ...

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8th International Conference on Artificial Intelligence and Security (ICAIS)

作者： Bo, Xiong Feng Wenlong Zhang, Jin Hainan Univ Sch Informat & Commun Engn Haikou 570228 Hainan Peoples R China Towson Univ Dept Comp & Informat Sci 8000 York Rd Towson MD 21252 USA

ISBN: (纸本)9783031067884;9783031067877

The traditional whale algorithm has some shortcomings such as low solution accuracy and falling into local optimization easily. Aiming at these, a new whale optimization algorithm (NWOA) based on the artificial fish swarm algorithm is proposed, which combined with the advantages of artificial fish swarm algorithm that the crowding factor is used to limit the cluster size to promote the algorithm to be more optimized widely. The new whale optimization algorithm introduces the concept of crowding in the artificial fish swarm algorithm (AFSA) into the whale algorithm and changes characteristics of whale populations, and it overcomes the shortcoming of the traditional whale algorithm that individual whales perform different foraging behaviors according to the size of the random probability number. By calculating the degree of crowding around the prey, the foraging behavior of individual whales is designed to improve the convergence speed and optimization accuracy of the whale algorithm and enhance the stability of the algorithm. The simulation experiment is conducted by selecting 9 benchmark test functions. The experimental results show that the convergence speed and algorithm stability of the algorithm in this paper are significantly improved compared to the traditional whale algorithm.

关键词： whale optimization algorithm Artificial fish swarm algorithm algorithm optimization Random probability number Crowdedness

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whale optimization algorithm Based Optimal Operation of Power Distribution Systems 57

Whale Optimization Algorithm Based Optimal Operation of Powe...

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57th International Universities Power Engineering Conference (UPEC) - Big Data and Smart Grids

作者： Baimakhanov, Olzhas Saukhimov, Almaz Ceylan, Oguzhan Almaty Univ Power Engn & Telecommun Elect Power Syst Dept Alma Ata Kazakhstan Marmara Univ Dept Elect & Elect Engn Istanbul Turkey

ISBN: (纸本)9781665455053

This paper solves the optimal operation problem of distribution systems using renewable energy sources and voltage regulators. The developed model makes use of whale optimization algorithm based optimization. We apply the proposed optimization model to a modified low voltage distribution system of Almaty, Kazakhstan. Three different cases were simulated. The cases considered are medium load level with medium PV power output, high load level with medium PV output, and high load level and low PV power outputs. The simulation results show that the proposed algorithm is able lower the active power losses.

关键词： distribution system optimization renewable energy sources whale optimization algorithm

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CWOA: A novel complex-valued encoding whale optimization algorithm

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MATHEMATICS AND COMPUTERS IN SIMULATION 2023年第1期207卷 151-188页

作者： Zhang, Jinzhong Zhang, Gang Kong, Min Zhang, Tan Wang, Duansong Chen, Rui West Anhui Univ Sch Elect & Optoelect Engn Luan 237012 Peoples R China

The whale optimization algorithm (WOA) is inspired by humpback whales' bubble-net assaulting mechanism and imitates behaviors such as shrinking and enveloping prey, spiral bubble-net assaulting prey, and variational searching for prey to determine the best solution. However, the basic WOA has the disadvantages of low computation precision, slow convergence rate and easily falling into search stagnation. To strengthen the optimization quality and search reliability, this paper presents a distinctive complex-valued encoding WOA (CWOA) to satisfy the function optimization and engineering design. The complex -valued methodology utilizes a diploid structure to encode individual whales, and the real and imaginary components are added to the basic WOA to revise the position of each humpback whale, which converts the two-dimensional encoding area to a one-dimensional expression area and utilizes the real and imaginary components to illustrate a feasible solution with inherent parallelism. This methodology enriches the population diversity, furthers the individual information, elevates the general search ability, avoids premature convergence and promotes convergence efficiency. The CWOA not only utilizes the characteristics of the complex-valued methodology to avoid slipping into local optima but also exhibits excellent adaptability and robustness to determine the accurate value. Sixteen benchmark test functions and eight engineering designs are used to verify the applicability and practicability of the CWOA. The experimental results demonstrate that the optimization productivity and search performance of the CWOA are superior to those of other algorithms. In addition, the CWOA is a more successful and efficacious method that completely stabilizes exploration and exploitation to establish a quicker convergence rate, higher computation precision, greater resilience and stability.(c) 2022 International Association for Mathematics and Computers in Simulation (IMACS). Published

关键词： whale optimization algorithm Complex-valued encoding Benchmark test functions Engineering design

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Hybrid whale optimization algorithm based on symbiosis strategy for global optimization

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APPLIED INTELLIGENCE 2023年第13期53卷 16663-16705页

作者： Li, Maodong Xu, Guang-hui Zeng, Liang Lai, Qiang Hubei Univ Technol Sch Elect & Elect Engn Hubei Key Lab High efficiency Utilizat Solar Energ Wuhan 430068 Peoples R China East China Jiaotong Univ Sch Elect & Automation Engn Nanchang 330013 Peoples R China

The whale optimization algorithm (WOA) is a simple structured and easily implemented swarm-based algorithm inspired by the unique bubble-net feeding method of humpback whales. Past studies have shown that WOA performs well in a number of optimization problems. However, it is difficult for WOA to completely free itself from the problems of insufficient convergence accuracy and premature convergence when solving global optimization problems. To address these issues, a hybrid whale optimization algorithm based on symbiotic strategy (HWOAMS) is proposed in this paper. The main idea of the proposed method is to combine the improved symbiotic organisms search algorithm (SOS) with the whale optimization algorithm thus enhancing the search ability of WOA. First, an improved symbiotic phase based on Levy flight and chaos strategy is introduced into the exploration process to enhance the global search capability;Second, an improved mutualism phase based on Brownian motion is used instead of the original shrinking encircling phase to achieve better local exploitation. Third, an improved parasitic phase based on a modified global optimal spiral operator strategy is embedded in the spiral updating position phase to help the algorithm further improve the exploitation efficiency and convergence accuracy. Finally, a global search strategy is proposed to help the algorithm better balance exploration and exploitation. To establish the effectiveness of the new algorithm, extensive simulation experiments are conducted on HWOAMS using the classical function test set, the CEC 2019 function set and four classical engineering problems. Numerical evaluation results indicate that HWOAMS outperforms 18 other algorithms in terms of local optimum avoidance ability and convergence accuracy in a majority of cases, and has better search performance.

关键词： whale optimization algorithm Symbiotic organisms search algorithm Diversity Global optimization problem

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Fuzzy Automatic Disturbance Rejection Control of Quadrotor UAV Based on Improved whale optimization algorithm

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IEEE ACCESS 2023年 11卷 69117-69130页

作者： Shan, Zebiao Wang, Yuhang Liu, Xiaosong Wei, Changbin Changchun Univ Sci & Technol Sch Elect & Informat Engn Changchun 130022 Peoples R China Jilin Univ Coll Commun Engn Changchun 130022 Peoples R China Changchun Meteorol Instrument Res Inst Changchun 130102 Peoples R China

With the aim of improving the rapidity and accuracy of quadrotor UAV positional trajectory tracking, this paper proposes a fuzzy automatic disturbance rejection control of quadrotor UAV based on improved whale optimization algorithm (IWOA-Fuzzy-ADRC). (1) To address the problems of low accuracy and slow convergence of the traditional whale algorithm, a combination of logistic chaos mapping and skew tent mapping is applied to improve the diversity of the initial population. (2) The global search capability is strengthened to avoid falling into local optimum through introducing the cross operator and Gaussian variational operators. (3) The IWOA is employed to iteratively optimize the adjustment coefficients of the fuzzy active disturbance rejection controller, the calculus gain of the nonlinear state error feedback control (NLSEF), and the error correction coefficients of the expansive state observer (ESO). The simulation results demonstrate that compared with the traditional ADRC controller and PID controller, the proposed IWOA-Fuzzy-ADRC controller can effectively improve the control performance of the system, accurately track the desired flight trajectory, accelerate the dynamic response of the control system, reduce the steady-state error, and enhance the anti-interference capability.

关键词： Quadrotors Control systems Heuristic algorithms Observers Autonomous aerial vehicles whale optimization algorithms Rotors Fuzzy systems Chaotic communication INDEX TERMS Gaussian processes UAV trajectory tracking fuzzy ADRC whale optimization algorithm chaotic mapping cross-operator Gaussian variant operators

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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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Optimal guidance whale optimization algorithm and hybrid deep learning networks for land use land cover classification

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EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING 2023年第1期2023卷 13页

作者： Vinaykumar, V. N. Babu, J. Ananda Frnda, Jaroslav Malnad Coll Engn Dept Informat Sci & Engn Hassan Karnataka India Univ Zilina Fac Operat & Econ Transport & Commun Dept Quantitat Methods & Econ Informat Zilina Slovakia Univ Zilina Univ Sci Pk Zilina Slovakia

Satellite Image classification provides information about land use land cover (LULC) and this is required in many applications such as Urban planning and environmental monitoring. Recently, deep learning techniques were applied for satellite image classification and achieved higher efficiency. The existing techniques in satellite image classification have limitations of overfitting problems due to the convolutional neural network (CNN) model generating more features. This research proposes the optimal guidance-whale optimization algorithm (OG-WOA) technique to select the relevant features and reduce the overfitting problem. The optimal guidance technique increases the exploitation of the search technique by changing the position of the search agent related to the best fitness value. This increase in exploitation helps to select the relevant features and avoid overfitting problems. The input images are normalized and applied to AlexNet-ResNet50 model for feature extraction. The OG-WOA technique is applied in extracted features to select relevant features. Finally, the selected features are processed for classification using Bi-directional long short-term memory (Bi-LSTM). The proposed OG-WOA-Bi-LSTM technique has an accuracy of 97.12% on AID, 99.34% on UCM, and 96.73% on NWPU, SceneNet model has accuracy of 89.58% on AID, and 95.21 on the NWPU dataset.

关键词： AlexNet Bi-directional long short-term memory Convolutional neural network Optimal guidance ResNet50 Satellite image classification whale optimization algorithm

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Exhaust emission reduction of a SI engine using acetone-gasoline fuel blends: Modeling, prediction, and whale optimization algorithm

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ENERGY REPORTS 2023年 9卷 77-86页

作者： Alahmer, Hussein Alahmer, Ali Alkhazaleh, Razan Alrbai, Mohammad Al Balqa Appl Univ Fac Artificial Intelligence Dept Automated Syst Al Salt 19117 Jordan Auburn Univ Dept Ind & Syst Engn Auburn AL 36849 USA Tafila Tech Univ Fac Engn Dept Mech Engn POB 179 Tafila 66110 Jordan Univ Jordan Sch Engn Dept Mech Engn Amman 11942 Jordan

Acetone-gasoline fuel is one potential favorable alternative option to regular gasoline fuel. This work attempts to mitigate pollution while simultaneously improving SI engine performance with acetone (AC) proportion in fuel mixture and varying engine speed. Acetone-gasoline alternative fuel was experimentally prepared by mixing 5-10 vol.% of acetone into ordinary gasoline. The experiments were performed in a spark ignition (SI) single-cylinder, four-stroke at variable engine speed. The engine performance was measured by an eddy current dynamometer (ECD), while the exhaust engine was measured by a gas analyzer connected to the gasoline engine. The proposed strategy, the integrated combined polynomial regression with whale optimization algorithm (WOA), was implemented. The results showed that the acetone blending of 10 % with gasoline (AC10: 10 vol. % acetone + 90 vol. % gasoline) decreases carbon monoxide (CO), unburned hydrocarbon (UHC), and nitrogen oxides (NOx), emissions by 26.3%, 30.3%, and 6.6%, respectively. Furthermore, the AC10 exhibited better engine brake power (BP) than pure gasoline, with an improvement of 4.39%. According to the optimization results, the BP was enhanced by AC10 to record 2.426 kW at 2887 rpm. In addition, engine emissions reduced to the lowest levels of 142.849 ppm, 0.426%, and 1088.178 ppm, in terms of UHC, CO, and NOx, respectively. The experiment results show that the WOA optimizer can accurately predict the optimal point in SI engine performance and emissions. (c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Acetone-gasoline blend SI engine Engine performance Emission control whale optimization algorithm Polynomial regression

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