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An efficient approach for optimum shape design of steel shear panel dampers under cyclic loading

SMART STRUCTURES AND SYSTEMS 2021年第3期27卷 547-557页

作者： Khatibinia, Mohsen Ahrari, Aghdas Gharehbaghi, Sadjad Sarafrazi, Seyyed Reza Univ Birjand Dept Civil Engn Birjand Iran Sharif Univ Technol Dept Civil Engn Tehran Iran

The low-cycle fatigue performance of shear panel damper (SPD) highly depends on the geometry of its shape and the criterion considered for its design. The main contribution of the current study is to find the optimum shape of the SPD subjected to cyclic loading by considering two different objective functions. The maximum equivalent plastic strain and the ratio of energy dissipation through plastic deformation to the maximum equivalent plastic strain are selected as the first and second objective functions, respectively. Since the optimization procedure requires high computational efforts, a hybrid computational approach is used to perform two paramount phases of estimating the inelastic responses of the SPD and solving the optimization problem. In the first phase, as an alternative for the time-consuming finite element analysis of the SPD, a weighted-support vector machine model is developed to predict the inelastic responses of the SPDs during the optimization process. In the second phase, the optimum shape of the SPD is found by using the whale optimization algorithm (WOA). The results indicate that both design criteria lead to the optimum-shaped SPDs with a significant improvement in their low cycle fatigue performance in comparing with the initial rectangular shape while a slight reduction in their energy dissipation capacity. Moreover, the second design criterion is slightly better in the performance improvement of the optimum-shaped SPDs compared with the first one. In addition, the weighted-based SVM approach can accurately predict the inelastic responses of the SPDs under cyclic loading, and its combination with WOA results in finding the optimum solutions quickly.

关键词： shear panel damper energy dissipation shape optimization support vector machine whale optimization algorithm

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Reliability-aware task scheduling for energy efficiency on heterogeneous multiprocessor systems

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JOURNAL OF SUPERCOMPUTING 2021年第10期77卷 11643-11681页

作者： Deng, Zexi Cao, Dunqian Shen, Hong Yan, Zihan Huang, Huimin Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou 510006 Peoples R China Guangxi Univ Nationalities Sch Sci Nanning 530006 Guangxi Peoples R China

Recent studies mainly focus on high performance or low power consumption for task scheduling on heterogeneous multiprocessor systems (HMSs). Dynamic voltage and frequency scaling (DVFS) is an important energy reduction technique, which adjusts the voltage and frequency of the processor while the task is executing. However, some studies have shown that reducing the voltage of processor increases the transient failure rate, which reduces system reliability. In this paper, we aim at addressing the scheduling problem of optimizing energy under makespan and reliability constraints on HMSs with DVFS. We first propose an improved whale optimization algorithm (WOA) deploying opposition-based learning and individual selection strategy, which can balance the exploration and exploitation ability. To maintain population diversity, we then apply a constrained rank-based method which retains some infeasible individuals in the population. Finally, we reschedule the Critical Path Nodes (CPNs) to further improve the performance of improved WOA. The main difference between our work and most previous works is that we study a new scheduling problem, and utilize an improved WOA algorithm integrating with rescheduling CPNs and a constrained rank-based method. Extensive experiments are conducted to evaluate our proposed algorithm, and the evaluation results show that our proposed algorithm is compelling in comparison with the state-of-the-art algorithms.

关键词： Task scheduling whale optimization algorithm Opposition-based learning Heterogeneous multiprocessor systems

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Fault feature extraction of rolling element bearing based on EVMD

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JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING 2021年第12期43卷 1-14页

作者： Zhu, Danchen Liu, Guoqiang He, Wei Yin, Bolong Naval Petty Officer Acad Bengbu 233012 Peoples R China Guangzhou Mech Engn Res Inst Co Ltd Equipment Condit Detect Inst Guangzhou 510700 Peoples R China

Aiming at the problem that the bearing fault signal is weak and usually interfered by the strong background noise, which makes the fault feature extraction very difficult, an enhanced variational mode decomposition (EVMD) technique is proposed. First, the autoregressive (AR) model was utilized to eliminate the stationary components in the signal in advance to reduce the noise interference and the maximum kurtosis of the residual signal was set as the target. Second, the maximum frequency-domain correlated kurtosis was adopted as the fitness value, and the decomposition modes K and quadratic penalty factor alpha in the VMD approach were adaptively selected by the whale optimization algorithm. Third, the reconstruction signal was acquired, then the enhanced envelope spectrum was employed to weaken the interference of irrelevant frequency components and the fault features of rolling element bearing could be extracted accurately. The results of simulation and experimental analysis show that the proposed algorithm can significantly reduce the noise interference and avoid the blindness selection of VMD parameters. The comparison with fix-parameter VMD and fast kurtogram approaches shows that the proposed technique can improve the effectiveness of defect signature extraction, which has a certain value for engineering application.

关键词： AR model whale optimization algorithm Variational mode decomposition Rolling element bearing Fault feature extraction

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Sustainable closed-loop supply chain network under uncertainty: a response to the COVID-19 pandemic

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2021年 1-17页

作者： Rafigh, Parisa Akbari, Ali Akbar Bidhandi, Hadi Mohammadi Kashan, Ali Husseinzadeh Islamic Azad Univ South Tehran Branch Dept Ind Engn Tehran Iran Tarbiat Modares Univ Fac Ind & Syst Engn Tehran Iran

This study proposes a sustainable closed-loop supply chain under uncertainty to create a response to the COVID-19 pandemic. In this paper, a novel stochastic optimization model integrating strategic and tactical decision-making is presented for the sustainable closed-loop supply chain network design problem. This paper for the first time implements the concept of sustainable closed-loop supply chain for the application of ventilators using a stochastic optimization model. To make the problem more realistic, most of the parameters are considered to be uncertain along with the normal probability distribution. Since the proposed model is more complex than majority of previous studies, a hybrid whale optimization algorithm as an enhanced metaheuristic is proposed to solve the proposed model. The efficiency of the proposed model is tested in an Iranian medical ventilator production and distribution network in the case of the COVID-19 pandemic. The results confirm the performance of the proposed algorithm in comparison with two other similar algorithms based on different multi-objective criteria. To show the impact of sustainability dimensions and COVID-19 pandemic for our proposed model, some sensitivity analyses are done. Generally, the findings confirm the performance of the proposed sustainable closed-loop supply chain for the pandemic cases like COVID-19.

关键词： Sustainable supply chain Stochastic programming Multi-objective optimization whale optimization algorithm COVID-19 pandemic

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A new methodology to improve wind power prediction accuracy considering power quality disturbance dimension reduction and elimination

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ENERGY 2024年 287卷

作者： Zheng, Xidong Bai, Feifei Zeng, Ziyang Jin, Tao Fuzhou Univ Coll Elect Engn & Automat Fuzhou 350108 Peoples R China Univ Queensland Sch Informat Technol & Elect Engn Brisbane Qld 4072 Australia Griffith Univ Sch Engn & Built Environm Gold Coast Qld 4222 Australia Fujian Prov Univ Engn Res Ctr Smart Distribut Grid Equipment Fuzhou 350108 Peoples R China

The proper integration of wind power-driven grids relies heavily on a reliable balance between electricity production and demand. Therefore, accurate prediction is essential for planning and efficient operation of wind power systems to ensure their continuous supply. However, increasingly severe power quality disturbance (PQD) constantly disturbs this equilibrium, which affects the accuracy of wind power prediction to a large extent. For this purpose, this paper developed a novel optimization methodology to improve wind power prediction accuracy considering micro PQD dimension reduction and elimination for wind-storage integrated systems. A novel idea has been presented in this optimization methodology to eliminate the barrier of PQD and wind power prediction. In the micro aspect, a PQD dimension reduction and elimination strategy based on dynamic mode decomposition (DMD) and Wiener Filter (WF) is proposed to eliminate the autonomy of PQD. This elimination of autonomy allows the WF to get a higher signal-to-noise ratio (SNR). In the macro aspect, this paper takes PQD of different complexity into full consideration, and compares their effects on improving wind power prediction accuracy based on deep learning-based approaches. Through the experimental verification, it is confirmed that the proposed DMD-WF optimization method has demonstrated an effective dimension reduction and elimination of PQD. Moreover, it is found that the proposed PQD optimization method contributes to improve the deep learning-based prediction accuracy when PQD is more complex. The proposed methodology creates a novel perspective to improve the short-term wind power prediction accuracy, which provides a theoretical and methodological guidance for future development of large-scale integrated wind-storage systems.

关键词： Power quality disturbance Dynamic mode decomposition Wiener filter whale optimization algorithm Attention mechanism Long short-term memory

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Multi-Objective Optimal Placement and Sizing of Distribution Static Compensator in Radial Distribution Networks With Variable Residential, Commercial and Industrial Demands Considering Reliability

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IEEE ACCESS 2021年 9卷 46911-46926页

作者： Noori, Alireza Zhang, Yiming Nouri, Negar Hajivand, Mohammad Beijing Univ Technol Coll Elect Informat & Control Engn Beijing 100124 Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Minist Sci Res & Technol Aerosp Res Inst Tehran *** Iran Islamic Azad Univ Borujerd Branch Young Researchers & Elite Club Borujerd *** Iran

This article presents an optimal placement and sizing of distribution static compensator (DSTATCOM) in radial distribution networks as multi-objective optimization with the objective of power loss reduction, voltage profile improvement and network reliability considering different variable demands using whale optimization algorithm (WOA). The WOA is inspired by the hunting behavior of humpback whales. The optimal location and size of DSTATCOM are considered as decision variables that are determined using the WOA. Network reliability is defined as energy not supplied (ENS) of the customers caused by line outages. The proposed method is implemented on IEEE 33-bus and Ahvaz 59-bus distribution networks. The optimal placement and sizing of DSTATCOM are investigated considering different residential, commercial and industrial loads, and also the effect of incorporating reliability is evaluated on power loss, minimum voltage and ENS. The simulation results show that the power loss and ENS are declined and also voltage profile is improved by determining the location and size of DSTATCOM in the networks in different loading conditions using the WOA. The obtained results demonstrate that considering reliability further reduces the ENS and more customer demand is met. The power loss decreases 21.60%, 30.71%, and 22.03% for residential, commercial and industrial loads in the 33-bus network and 26.86%, 31.94%, and 24.39% for the 69-bus network, respectively compared to base networks. Moreover, the performance of the WOA is compared with previous studies and the results indicate the superiority of the proposed method.

关键词： optimization Distribution networks Reliability Capacitors Reactive power Resource management algorithms Radial distribution network distribution static compensator optimal placement and sizing variable demand reliability whale optimization algorithm

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Improving Localization Success Rate of Three Magnetic Targets Using Individual Memory-Based WO-LM algorithm

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IEEE SENSORS JOURNAL 2021年第19期21卷 21750-21758页

作者： Lv, Bowen Qin, Yanding Dai, Houde Su, Shijian Nankai Univ Coll Artificial Intelligence Tianjin 300350 Peoples R China Chinese Acad Sci Quanzhou Inst Equipment Mfg Haixi Inst Jinjiang 362216 Peoples R China

Magnetic localization technique has the advantages of millimeter-level localization accuracy, fast localization speed, and no limit-of-sight constraints. However, most studies are limited to the tracking of single or two magnets because of the low localization success rate for multiple magnets, thereby restricting its application scenarios. In this study, a novel multi-magnetic target localization method, named individual memory-based whale optimization and Levenberg-Marquardt (IMWO-LM) algorithm, is proposed to overcome the problem of low localization success rate. The IMWO algorithm adds the individual historical optimal location memory to each whale, which improves the global search ability of the population. This IMWO algorithm result is employed as the initial guess of the LM method. Afterward, the LM result is adopted as the initial guess of the LM method in the subsequent localization process. In 20 groups of 500 localizations, the average localization success rates of the particle swarm optimization-LM algorithm, whale optimization-LM algorithm, and the proposed IMWO-LM algorithm were 85.1%, 83.82%, and 98.28%, respectively. In the experiment of locating three magnetic targets simultaneously, the average position error and the average orientation error were 3.46 mm and 6.77 degrees, respectively. Results show that this method can significantly improve the localization success rate while maintaining high localization accuracy.

关键词： Location awareness Magnetometers Magnetic flux whales Permanent magnets optimization Magnetic sensors Magnetic localization multi-target localization whale optimization algorithm individual memory

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A hybrid GEP and WOA approach to estimate the optimal penetration rate of TBM in granitic rock mass

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SOFT COMPUTING 2021年第17期25卷 11877-11895页

作者： Li, Zimu Bejarbaneh, Behnam Yazdani Asteris, Panagiotis G. Koopialipoor, Mohammadreza Armaghani, Danial Jahed Tahir, M. M. Southwest Univ Sch Comp & Informat Sci Chongqing 400715 Peoples R China Univ Teknol Malaysia Fac Civil Engn Dept Geotech & Transportat Skudai 81310 Johor Malaysia Sch Pedag & Technol Educ Computat Mech Lab Athens 14121 Greece Amirkabir Univ Technol Fac Civil & Environm Engn Tehran 15914 Iran Univ Malaya Fac Engn Dept Civil Engn Kuala Lumpur 50603 Malaysia Univ Teknol Malaysia UTM Construct Res Ctr Sch Civil Engn Inst Smart Infrastruct & Innovat Construct ISI Johor Baharu 81310 Johor Malaysia

The efficiency of tunnel boring machines (TBMs) in underground projects has great significance for the mining and tunneling industries, demanding a reliable estimation of the TBM's performance in different geotechnical conditions. The current research work attempted to suggest an optimal predictor model of TBM performance as a reliable alternative to experimental and numerical techniques. To achieve this target, three data-mining techniques, namely neural network (NN), gene expression programming (GEP), and multivariate adaptive regression splines (MARS), were employed for modelling the TBM performance, and then the most robust predictive model was optimized via a metaheuristic search method known as whale optimization algorithm (WOA). For the modelling purpose, an experimental database was compiled by performing a field assessment program in a tunneling project in Malaysia and then conducting laboratory testing on the derived rock specimens. Based on the measured experimental data, the six most influential parameters were identified and served as model inputs to predict penetration rate (PR). In order to indicate the capability of the developed GEP and NN models, a stepwise linear regression model, i.e., MARS, was designed for PR prediction as well. The predictive capacity of the constructed models was quantified using a series of statistical indices, i.e., root mean squared error (RMSE), determination coefficient (R-2) and variance account for (VAF). Based on the computed indices for testing records, both the proposed GEP and NN models (with RMSE values of 0.1882 and 0.2120 and R-2 values of 0.9058 and 0.8735, respectively) yielded more accurate predictive results than the MARS model with RMSE of 0.2553 and R-2 of 0.8346. Hence, by achieving the most robust performance compared to the rest, GEP-based model can provide a new practical equation with a high level of accuracy. In other part of this study, the six input parameters of the GEP model and its equation were

关键词： TBM performance Penetration rate Gene expression programming Neural network Multivariate adaptive regression splines whale optimization algorithm

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Analysis of Third-Order Nonlinear Multi-Singular Emden-Fowler Equation by Using the LeNN-WOA-NM algorithm

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IEEE ACCESS 2021年 9卷 72111-72138页

作者： Zhang, Yin Lin, Jianqiang Hu, Zhenhuan Khan, Naveed Ahmad Sulaiman, Muhammad South China Inst Software Engn Dept Elect Guangzhou 510000 Peoples R China Guangxi Univ Sci & Technol Sch Elect & Informat Engn Liuzhou 545000 Peoples R China Abdul Wali Khan Univ Dept Math Mardan 23200 Pakistan

In this paper, a novel soft computing algorithm is designed for the numerical solution of third-order nonlinear multi-singular Emden-Fowler equation (TONMS-EFE) using the strength of universal approximation capabilities of Legendre polynomials based Legendre neural networks supported with optimization power of the whale optimization algorithm (WOA) and Nelder-Mead (NM) algorithm. Unsupervised error functions are constructed in terms of mean square error for governing TONMS-EF equations of first and second order. Unknown designed parameters in LeNN structure are optimized initially by WOA for global search while NM algorithm further enhances the rapid local search convergence. The proposed algorithm's objective is to show the accuracy and robustness in solving challenging problems like TONMS-EFE. To study our designed scheme's performance and effectiveness, LeNN-WOA-NM is implemented on four cases of TONMS-EFE. The results obtained by the proposed algorithm are compared with the Particle Swarm optimization (PSO) algorithm, Cuckoo search algorithm (CSA), and WOA. Extensive graphical and statistical analysis for fitness value, absolute errors, and performance indicators in terms of mean, median, and standard deviations show the proposed algorithm's efficiency and accuracy.

关键词： Mathematical model whales optimization Approximation algorithms Sociology Shape Neural networks Singular Emden– Fowler equation soft computing algorithm weighted legendre neural networks Nelder-Mead algorithm whale optimization algorithm

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An efficient population-based multi-objective task scheduling approach in fog computing systems

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JOURNAL OF CLOUD COMPUTING-ADVANCES SYSTEMS AND APPLICATIONS 2021年第1期10卷 1-31页

作者： Movahedi, Zahra Defude, Bruno Hosseininia, Amir Mohammad Univ Tehran Coll Farabi Dept Engn Tehran Iran Inst Polytech Paris Telecom SudParis SAMOVAR Palaiseau France

With the rapid development of Internet of Things (IoT) technologies, fog computing has emerged as an extension to the cloud computing that relies on fog nodes with distributed resources at the edge of network. Fog nodes offer computing and storage resources opportunities to resource-less IoT devices which are not capable to support IoT applications with computation-intensive requirements. Furthermore, the closeness of fog nodes to IoT devices satisfies the low-latency requirements of IoT applications. However, due to the high IoT task offloading requests and fog resource limitations, providing an optimal task scheduling solution that considers a number of quality metrics is essential. In this paper, we address the task scheduling problem with the aim of optimizing the time and energy consumption as two QoS parameters in the fog context. First, we present a fog-based architecture for handling the task scheduling requests to provide the optimal solutions. Second, we formulate the task scheduling problem as an Integer Linear Programming (ILP) optimization model considering both time and fog energy consumption. Finally, we propose an advanced approach called Opposition-based Chaotic whale optimization algorithm (OppoCWOA) to enhance the performance of the original WOA for solving the modelled task scheduling problem in a timely manner. The efficiency of the proposed OppoCWOA is shown by providing extensive simulations and comparisons with the original WOA and some existing meta-heuristic algorithms such as Artificial Bee Colony (ABC), Particle Swarm optimization (PSO), and Genetic algorithm (GA).

关键词： Fog computing Task scheduling Internet of things Meta-heuristic whale optimization algorithm Opposition-based learning Chaos theory

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