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Vibration control in buildings under seismic excitation using optimized tuned mass dampers

FRATTURA ED INTEGRITA STRUTTURALE-FRACTURE AND STRUCTURAL INTEGRITY 2020年第54期14卷 66-87页

作者： Brandao, Francisco da Silva Fadel Miguel, Leticia Fleck PPGEC UFRGS Porto Alegre RS Brazil Ctr Univ Ritter Reis UniRitter Porto Alegre RS Brazil Univ Fed Rio Grande do Sul Dept Mech Engn Porto Alegre RS Brazil

Earthquakes can cause vibration problems in many types of structures, generating large displacements. The interstory drift is a design criterion very used in seismic analysis and the structural control is an alternative to reduce these displacements and improve the performance of these structures adapting them to the imposed criteria. TMD is a device widely used due to the simple principle of operation and many successful applications in real life practice. This paper investigates the use of optimized TMD for reduction of maximum horizontal displacement at the top floor and interstory drift of a steel building under seismic excitation considering three scenarios: single TMD at the top floor;MTMD horizontally arranged at the top floor;and MTMD vertically arranged on the structure. By a metaheuristic optimization algorithm, the parameters and positions of the devices are obtained. Three real and one artificial earthquakes are employed in the simulations. The results showed that all proposed scenarios are efficient in reducing top floor response and interstory drift to values below of the interstory drift limits allowed by the standard code consulted. However, Scenario 2 presented the best reduction for the top displacement and interstory drift to the critical floor for the worst earthquake considered.

关键词： TMD Vibration control optimization whale optimization algorithm Interstory drift

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Integration of hybrid communication and remote sensing for ITS application

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TELECOMMUNICATION SYSTEMS 2020年第4期74卷 511-529页

作者： Guchhait, Arghya Maji, B. Kandar, D. NIT Durgapur Dept Elect & Commun Engn Durgapur India NIIT Univ Dept Elect & Commun Engn Neemrana India North East Hill Univ Dept Informat Technol Shillong Meghalaya India

Intelligent transportation system is a prominent technology solution for the vehicle manufacturers in this present scenario. The manufacturers are trying to deploy an advanced system for automation through sensing. Various technologies are being used to communicate with other near-by vehicle and to sense their perspective on the road. The important problem is to share the radar and communication information with other vehicles without any time delay. The vehicles uses a different hardware system for establishing radar sensing and communication concurrently, which increases the cost and bandwidth requirement of the overall system. Instead of using multiple system and hardware separately for communication and remote sensing, a single hybrid system might be a cost effective solution which will serve both communication and remote sensing requirements. This work proposes a concept for integrating radar sensing and hybrid communication system (IRSHCS) with multiple input multiple output (MIMO) orthogonal frequency division multiplexing (OFDM) in a single hardware known as software defined radio. It serves simultaneous remote sensing and communication to the vehicle user without delay with the dedicated short range communication system and worldwide interoperability for microwave access. Moreover, an optimization problem is formulated to improve the performance of IRSHCS, which focuses on sharing both the radar and communication between the vehicles without any time delay by satisfying the radar and communication system requirements with suitable parameters. The whale optimization algorithm generates an optimal solution to solve the limitations of the hybrid system by choosing maximum channel capacity as its objective function. Experimental results are evaluated to demonstrate the IRSHCS MIMO OFDM system provide better performance as compared to the conventional approaches.

关键词： Intelligent transportation system Radar system Multiple input multiple output Software defined radio whale optimization algorithm

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A novel WOA-based structural damage identification using weighted modal data and flexibility assurance criterion

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STRUCTURAL ENGINEERING AND MECHANICS 2020年第4期75卷 445-454页

作者： Chen, Zexiang Yu, Ling Jinan Univ Sch Mech & Construct Engn MOE Key Lab Disaster Forecast & Control Engn Guangzhou 510632 Peoples R China

Structural damage identification (SDI) is a crucial step in structural health monitoring. However, some of the existing SDI methods cannot provide enough identification accuracy and efficiency in practice. A novel whale optimization algorithm (WOA) based method is proposed for SDI by weighting modal data and flexibility assurance criterion in this study. At first, the SDI problem is mathematically converted into a constrained optimization problem. Unlike traditional objective function defined using frequencies and mode shapes, a new objective function on the SDI problem is formulated by weighting both modal data and flexibility assurance criterion. Then, the WOA method, due to its good performance of fast convergence and global searching ability, is adopted to provide an accurate solution to the SDI problem, different predator mechanisms are formulated and their probability thresholds are selected. Finally, the performance of the proposed method is assessed by numerical simulations on a simply-supported beam and a 31-bar truss structures. For the given multiple structural damage conditions under environmental noises, the WOA-based SDI method can effectively locate structural damages and accurately estimate severities of damages. Compared with other optimization methods, such as particle swarm optimization and dragonfly algorithm, the proposed WOA-based method outperforms in accuracy and efficiency, which can provide a more effective and potential tool for the SDI problem.

关键词： structural damage identification whale optimization algorithm constrained optimization problem objective function flexibility assurance criterion

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An Improved Differential Evolution whale algorithm for Economic Load Distribution

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Journal of Computer and Communications 2022年第10期10卷 88-103页

作者： Haiming Li Chunning Fu School of Computer Science and Technology Shanghai University of Electric Power Shanghai China School of Computer Science University of Nottingham Nottingham Park Nottingham UK

An improved optimization algorithm combining the differential evolution algorithm and the whale algorithm is proposed for the problem of not being able to get rid of the local optimum in the economic load distribution algorithm. The algorithm adopts a nonlinear convergence strategy, a crossover strategy of differential evolution and the introduction of an elimination mechanism, which balances the global search and local exploitation ability of the algorithm and improves the accuracy of the solved optimal solution. The 13-unit and 40-unit systems are selected for economic load distribution calculation, and the experimental results show that the proposed improved algorithm is superior in distributing the economic load of the power system and can effectively reduce the economic cost.

关键词： whale optimization algorithm Differential Evolution algorithm Elimination Mechanism Economic Load Distribution

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A novel parameter estimation in dynamic model via fuzzy swarm intelligence and chaos theory for faults in wastewater treatment plant

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SOFT COMPUTING 2020年第1期24卷 111-129页

作者： Anter, Ahmed M. Gupta, Deepak Castillo, Oscar Beni Suef Univ Fac Comp & Informat Bani Suwayf 62511 Egypt Maharaja Agrsen Inst Technol Delhi India Tijuana Inst Technol Tijuana Mexico

Faults during a wastewater treatment for plant (WWTP) are critical issue for social and biological. Poorly treated wastewater may achieve dangerous effect for human as well as nature. This paper proposed a novel model based on a binary version of whale optimization algorithm (WOA), chaos theory and fuzzy logic, namely (CF-BWOA). CF-BWOA is applied in the application of WWTP to find out the more relevant attributes from the whole dataset, reducing cost and validation of decision rules, and helping to identify a non-well-structured domain. CF-BWOA attempts to reduce the whole feature set without loss of significant information to the classification process. Fast fuzzy c-means is used as a cost function to measure the fuzzification and uncertainty of data. Ten different chaos sequence maps are used to estimate and tune WOA parameters. Experiments are applied on a complex real-time dataset with various uncertainty features and missing values. The overall result indicates that the CWOA with the Sine chaos map shows the better performance, lower error, higher convergence speed and shorter execution time. In addition, the proposed model is capable of detecting sensor process faults in WWTP with high accuracy and can guide the operators of these systems to control decisions.

关键词： Fault detection whale optimization algorithm Wastewater treatment Chaos theory Fuzzy c-means algorithm

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Skin cancer diagnosis based on optimized convolutional neural network

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ARTIFICIAL INTELLIGENCE IN MEDICINE 2020年第0期102卷 101756-000页

作者： Zhang, Ni Cai, Yi-Xin Wang, Yong-Yong Tian, Yi-Tao Wang, Xiao-Li Badami, Benjamin Huazhong Univ Sci & Technol Tongji Hosp Tongji Med Coll Dept Throrac Surg 1095 Jiefang Ave Wuhan 430030 Hubei Peoples R China Huazhong Univ Sci & Technol Tongji Hosp Tongji Med Coll Canc Biol Res Ctr 1095 Jiefang Ave Wuhan 430030 Hubei Peoples R China Univ Georgia Athens GA 30602 USA

Early detection of skin cancer is very important and can prevent some skin cancers, such as focal cell carcinoma and melanoma. Although there are several reasons that have bad impacts on the detection precision. Recently, the utilization of image processing and machine vision in medical applications is increasing. In this paper, a new image processing based method has been proposed for the early detection of skin cancer. The method utilizes an optimal Convolutional neural network (CNN) for this purpose. In this paper, improved whale optimization algorithm is utilized for optimizing the CNN. For evaluation of the proposed method, it is compared with some different methods on two different datasets. Simulation results show that the proposed method has superiority toward the other compared methods.

关键词： Skin cancer diagnosis Deep learning Convolutional neural networks whale optimization algorithm Levy flight

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Augmented whale feature selection for IoT attacks: Structure, analysis and applications

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2020年 112卷 18-40页

作者： Mafarja, Majdi Heidari, Ali Asghar Habib, Maria Faris, Hossam Thaher, Thaer Aljarah, Ibrahim Birzeit Univ Dept Comp Sci Birzeit Palestine Univ Tehran Coll Engn Sch Surveying & Geospatial Engn Tehran Iran Natl Univ Singapore Sch Comp Dept Comp Sci Singapore Singapore Univ Jordan King Abdullah II Sch Informat Technol Amman Jordan Al Quds Univ Dept Comp Sci Jerusalem Palestine Palestine Polytech Univ Dept Comp Sci & Engn Hebron Palestine

Smart connected appliances expand the boundaries of the conventional Internet into the new Internet of Things (IoT). IoT started to hold a significant role in our life, and in several fields as in transportation, industry, smart homes, and cities. However, one of the critical issues is how to protect IoT environments and prevent intrusions. Attacks detection systems aim to identify malicious patterns and threats that cannot be detected by traditional security countermeasures. In literature, feature selection or dimensionality reduction has been profoundly studied and applied to the design of intrusion detection systems. In this paper, we present a novel wrapper feature selection approach based on augmented whale optimization algorithm (WOA), which adopted in the context of IoT attacks detection and handles the high dimensionality of the problem. In our approach, we introduce the use of both V-shaped and S-shaped transfer functions into WOA and compare the superior variant with other well-known evolutionary optimizers. The experiments are conducted using N-BaIoT dataset;wherein, five datasets were sampled from the original dataset. The dataset represents real IoT traffic, which is drawn from the UCI repository. The experimental results show that WOA based on V-shaped transfer function combined with elitist tournament binarization method is superior over S-shaped transfer function and outperforms other well-regarded evolutionary optimizers based on the obtained average accuracy, fitness, number of features, running time and convergence curves. Hence, we can conclude that the proposed approach can be deployed in IoT intrusion detection systems. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Internet of things whale optimization algorithm Feature selection Attacks detection Classification

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A new whale optimisation algorithm based on self-adapting parameter adjustment and mix mutation strategy

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INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING 2020年第10-11期33卷 949-961页

作者： Tong, Wangyu Wuhan Univ Technol Sch Comp Sci & Technol Wuhan Peoples R China Hubei Univ Technol Sch Comp Sci Wuhan Peoples R China

The whale optimization algorithm (WOA), which mimics the social behavior of humpback whales, is a recently developed meta-heuristic algorithm. However, WOA exists the defect of easily falling into local optimum. This paper proposes a new WOA based on a self-adapting parameter adjustment and a mix mutation strategy (abbreviated as SMWOA). The self-adapting parameter adjustment strategy based on a normal distribution is adopted to make the algorithm jump out of local optima and enhance the global exploration capability. Meanwhile, in order to obtain a better tradeoff between the exploration and exploitation capabilities of the WOA, a novel mix mutation strategy is embedded in the proposed algorithm. The performance of SMWOA is tested on 23 benchmark functions and test suites composed of four engineering design problems. Experimental results and statistical analyses indicate that the proposed algorithm is very competitive when compared to the original WOA as well as some state-of-the-art algorithms.

关键词： Engineering design problems exploration and Exploitation function optimization problems whale optimization algorithm

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An improved long short-term memory networks with Takagi-Sugeno fuzzy for traffic speed prediction considering abnormal traffic situation

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COMPUTATIONAL INTELLIGENCE 2020年第3期36卷 964-993页

作者： George, Shiju Santra, Ajit Kumar Vellore Inst Technol Sch Informat Technol & Engn Vellore Tamil Nadu India Amal Jyothi Coll Engn Kottayam Kerala India

Traffic speed prediction is an emerging paradigm for achieving a better transportation system in smart cities and improving the heavy traffic management in the intelligent transportation system (ITS). The accurate traffic speed prediction is affected by many contextual factors such as abnormal traffic conditions, traffic incidents, lane closures due to construction or events, and traffic congestion. To overcome these problems, we propose a new method named fuzzy optimized long short-term memory (FOLSTM) neural network for long-term traffic speed prediction. FOLSTM technique is a hybrid method composed of computational intelligence (CI), machine learning (ML), and metaheuristic techniques, capable of predicting the speed for macroscopic traffic key parameters. First, the proposed hybrid unsupervised learning method, agglomerated hierarchical K-means (AHK) clustering, divides the input samples into a group of clusters. Second, based on parameters the Gaussian bell-shaped fuzzy membership function calculates the degree of membership (high, low, and medium) for each cluster using Takagi-Sugeno fuzzy rules. Finally, the whale optimization algorithm (WOA) is used in LSTM to optimize the parameters obtained by fuzzy rules and calculate the optimal weight value. FOLSTM evaluates the accurate traffic speed from the abnormal traffic data to overcome the nonlinear characteristics. Experimental results demonstrated that our proposed method outperforms the state-of-the-art approaches in terms of metrics such as mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE).

关键词： agglomerative hierarchical K-means clustering fuzzy optimized long short-term memory Gaussian bell-shaped membership function traffic speed prediction whale optimization algorithm

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Prediction of bank telephone marketing results based on improved whale algorithms optimizing S_Kohonen network

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APPLIED SOFT COMPUTING 2020年 92卷 106259-106259页

作者： Yan, Chun Li, Meixuan Liu, Wei Shandong Univ Sci & Technol Coll Math & Syst Sci Qingdao 266590 Peoples R China Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao 266590 Peoples R China

Time deposit has the characteristics of strong stability and low cost. It is a stable source of funds for banks. In this paper, S_Kohonen network is used to predict the success rate of fixed deposit in bank telephone marketing. Firstly, the output layer is added after the competition layer of unsupervised Kohonen network, which makes Kohonen network become S_Kohonen network with supervised learning. Because the improved S_Kohonen network is similar to other feedforward neural networks, each adjacent layer is connected by weights, and the initial weights are random, which easily leads to the unstable output of the network, and still has the disadvantage of relatively low prediction accuracy. Therefore, an improved whale optimization algorithm (IWOA) is proposed to optimize the weights between the input layer and the competition layer of S_Kohonen network. In this paper, the inertia weight of whale optimization algorithm is introduced into random factor on the basis of non-linear decline, and then the random search pattern of Levy flight is introduced into whale algorithm. Finally, the empirical results show that the improved S_Kohonen network can more intuitively represent the classification results of the network, and the classification accuracy of S_Kohonen network optimized by IWOA is significantly higher than that of S_Kohonen network optimized by GA, WOA and LWOA algorithm. (C) 2020 Elsevier B.V. All rights reserved.

关键词： whale optimization algorithm Kohonen neural network Supervised learning

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