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Estimation of component values of filter design using optimization algorithms

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2019年第3期37卷 3567-3579页

作者： Nayak, Byamakesh Choudhury, Tanmoy Roy Misra, Banishree Mohapatra, Alivarani KIIT Sch Elect Engn Bhubaneswar India

In this paper, component values of analog active filters are selected based on the manufacturer's values of E series. The selection is based on optimization algorithms and here one is nature-inspired meta-heuristic optimization algorithm, called whale optimization algorithm (WOA), and another one is the physics-based method called Sine Cosine algorithm (SCA), are used for active filter design. The capability of optimization of the above algorithms is evaluated by considering the two active filters of a 4th order Butterworth and State variable filter. The performances of each algorithm are analyzed by applying to above two different filter structures, where the component values are determined by making compatible with different E series manufacturer.

关键词： Active filter design E series sine cosine algorithm whale optimization algorithm 4th order butterworth filter state variable filter

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Face Expression Recognition with the optimization based Multi-SVNN Classifier and the Modified LDP Features

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JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 2019年 62卷 43-55页

作者： Revina, I. Michael Emmanuel, W. R. Sam Manonmaniam Sundaranar Univ Nesamony Mem Christian Coll Dept Comp Sci Tirunelveli 627012 Tamil Nadu India

Facial expression recognition (FER) is the interesting research area that enables us to recognize the expression of the human face in the day-to-day life. Most of the traditional methods fail to recognize the expressions accurately as the expressions are based on the movements of the parts in the human face. The paper proposes the effective method of FER using the proposed whale- Grasshopper optimization algorithm based Multi-Support Vector Neural Network (W-GOA-based MultiSVNN). The features from the facial image is extracted using the Scale-Invariant Feature Transform (SIFT) and the proposed Scatter Local Directional Pattern (SLDP). The extracted features are classified using the proposed classifier to recognize the expression of the face. The proposed method of facial recognition enhances the recognition accuracy. The experimentation of the proposed algorithm is performed using the databases, such as Cohn-Kanade AU-Coded Expression Database and The Japanese Female Facial Expression (JAFFE) Database. The proposed algorithm outperforms the existing methods in terms of the accuracy, TPR, and FPR and the values are found to be 0.96, 0.96, and 0.009, respectively. (C) 2019 Elsevier Inc. All rights reserved.

关键词： Facial expression recognition MultiSVNN classifier Grasshopper optimization algorithm whale optimization algorithm Local Directional Pattern

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Swarm intelligence RFID network planning using multi-antenna readers for asset tracking in hospital environments

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COMPUTER NETWORKS 2021年 198卷 108427-108427页

作者： Shokouhifar, Mohammad Shahid Beheshti Univ Dept Elect & Comp Engn Tehran Iran

Radio frequency identification (RFID) systems have been widely used for the inventory management and asset tracking in hospital industry. Although the total cost of an RFID system relies mostly on the number of readers, existing RFID network planning (RNP) techniques focused on single-port readers. This paper introduces a swarm intelligence RFID network planning model with multi-antenna readers (named RNP-MAR) to efficiently track medical assets in hospital environments. Our motivation is to utilize multi-port RFID readers, in order to minimize the total cost of the RFID network, and simultaneously maximizing the network coverage. To achieve this purpose, we consider three reader types with one, two, and four antenna ports. Moreover, different antenna models with different coverage characteristics are utilized to be allocated on the different ports of RFID readers. The aim of the RNP-MAR is not only to determine the optimal placements of the RFID readers in the hospital plan, but also to design the multi-antenna readers and their antennas. To efficiently solve the RNP-MAR model, a combined global-local linear-nonlinear metaheuristic based on whale optimization algorithm and simulated annealing (named WOA-SA) is presented. The multi-objective function of the WOA-SA is to maximize the network coverage, while minimizing the total cost, interference, collision, and power consumption. Simulation results show that the proposed method provides 39.57% saving (on average) in the total cost of the RFID network through efficiently utilizing multi-antenna RFID readers.

关键词： RFID network planning Asset tracking Directional antennas Multi-antenna readers whale optimization algorithm Simulated annealing

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Joint optimization method for task scheduling time and energy consumption in mobile cloud computing environment

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APPLIED SOFT COMPUTING 2019年 80卷 534-545页

作者： Peng, Hua Wen, Wu-Shao Tseng, Ming-Lang Li, Ling-Ling Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou 510006 Guangdong Peoples R China Asia Univ Inst Innovat & Circular Econ Taichung Taiwan China Med Univ Hosp Dept Med Res Taichung Taiwan Hebei Univ Technol State Key Lab Reliabil & Intelligence Elect Equip Tianjin 300130 Peoples R China

An unheard of growth in mobile data traffic has drawn attention from academia and industry. Mobile cloud computing is an emerging computing paradigm combining cloud computing and mobile networks to alleviate resource-constrained limitations of mobile devices, which can greatly improve network quality of service and efficiency to make good use of available network resource. Mobile cloud computing not only inherits the advantages of strong computing capacity and massive storage of cloud computing, but also overcomes the time and geographical restrictions, bringing benefits for mobile users to offload complex computation to powerful cloud servers for execution anytime and anywhere. To this end, an optimal task workflow scheduling scheme is proposed for the mobile devices, based on the dynamic voltage and frequency scaling technique and the whale optimization algorithm. Through considering three factors: task execution position, task execution sequence, and operating voltage and frequency of mobile devices, this study makes a tradeoff between performance and energy consumption by solving the joint optimization for task completion time and energy consumption simultaneously. Finally, a series of extensive simulation results has demonstrated and verified the scheme has distinguished performance in terms of efficiency and operational cost, providing feasible solutions to similar optimization problems of mobile cloud computing. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Mobile cloud computing Workflow Multi-objective joint optimization whale optimization algorithm

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Multi-strategy boosted mutative whale-inspired optimization approaches

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APPLIED MATHEMATICAL MODELLING 2019年 73卷 109-123页

作者： Luo, Jie Chen, Huiling Heidari, Ali Asghar Xu, Yueting Zhang, Qian Li, Chengye Wenzhou Univ Dept Comp Sci Wenzhou 325035 Peoples R China Univ Tehran Coll Engn Sch Surveying & Geospatial Engn Tehran *** Iran Natl Univ Singapore Sch Comp Dept Comp Sci Singapore 117417 Singapore Wenzhou Med Univ Affiliated Hosp 1 Dept Pulm & Crit Care Med Wenzhou 325000 Peoples R China

This paper presents an improved whale optimization algorithm (WOA) for global optimization. WOA is a recently introduced meta-heuristic algorithm mimicking the hunting behavior of humpback whales. Owing to its simplicity in exploratory and exploitative operators and the satisfactory efficacy, this algorithm has found its place among the well-established population-based approach utilized in many engineering and science areas. However, this method is easy to fall into local optimum when dealing with some optimization cases. In order to further enhance its exploratory and exploitative performance, three strategies are incorporated into the original method to keep a better balance between exploitation and exploration tendencies. First, the chaotic initialization phase is introduced into the optimizer to initiate the swarm of chaos-triggered whales. Then, Gaussian mutation is employed to intensify the diversity level of the evolving population. At last, a chaotic local search with a 'shrinking' strategy is used to enhance the exploitative leanings of the basic optimizer. In order to verify the effectiveness of the improved WOA, it is compared to four meta-heuristic and state-of-the-art evolutionary algorithms on representative benchmark functions. Trial results and simulations reveal that not only the proposed improved WOA is significantly better than those basic algorithms including original WOA but also it is superior to compared state-of-the-art approaches. Moreover, the proposed algorithm is successfully applied to realize three constrained engineering test cases, which the results suggest that the improved WOA can effectively deal with the constrained functions as well. (C) 2019 Elsevier Inc. All rights reserved.

关键词： whale optimization algorithm Function optimization Chaotic local search Gaussian mutation Chaotic initialization

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A transient electromagnetic signal denoising method based on an improved variational mode decomposition algorithm

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MEASUREMENT 2021年 184卷 109815-109815页

作者： Feng, Guorui Wei, Huiru Qi, Tingye Pei, Xiaoming Wang, Hong Taiyuan Univ Technol Coll Min Engn Taiyuan Peoples R China Res Ctr Green Min Engn Technol Shanxi Prov Taiyuan Peoples R China Shanxi Feida Technol Co Ltd Jinzhong Peoples R China

Transient electromagnetic method (TEM) is often disturbed by surrounding noise in field measurement, which leads to low resolution of data and affects accurate positioning of geological anomalies. In order to solve this problem, an improved variational mode decomposition (VMD) algorithm based on whale optimization algorithm (WOA) is proposed. Firstly, the WOA is used to quickly obtain the optimal parameter combination of the decomposition number K and the penalty factor alpha of the VMD algorithm. Then, the improved VMD is used to adaptively decompose the signal. Finally, the Bhattacharyya distance algorithm is used to identify the effective mode and noise mode to accurately reconstruct the signal. The simulation comparison test shows that the proposed method has better performance in the noise processing of TEM signals. In addition, in the field experiment, this method successfully filters out the noise modes in the signal, which greatly improves the accuracy of data interpretation and verifies the effectiveness of the algorithm.

关键词： Transient electromagnetic signal Electromagnetic noise Variational mode decomposition Parameter optimization whale optimization algorithm

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A Tri-Stage Wrapper-Filter Feature Selection Framework for Disease Classification

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SENSORS 2021年第16期21卷 5571-5571页

作者： Mandal, Moumita Singh, Pawan Kumar Ijaz, Muhammad Fazal Shafi, Jana Sarkar, Ram Jadavpur Univ Dept Comp Sci & Engn Kolkata 700032 India Jadavpur Univ Dept Informat Technol Kolkata 700106 India Sejong Univ Dept Intelligent Mechatron Engn Seoul 05006 South Korea Prince Sattam Bin Abdul Aziz Univ Coll Arts & Sci Dept Comp Sci Wadi Ad Dwasir 11991 Saudi Arabia

In machine learning and data science, feature selection is considered as a crucial step of data preprocessing. When we directly apply the raw data for classification or clustering purposes, sometimes we observe that the learning algorithms do not perform well. One possible reason for this is the presence of redundant, noisy, and non-informative features or attributes in the datasets. Hence, feature selection methods are used to identify the subset of relevant features that can maximize the model performance. Moreover, due to reduction in feature dimension, both training time and storage required by the model can be reduced as well. In this paper, we present a tri-stage wrapper-filter-based feature selection framework for the purpose of medical report-based disease detection. In the first stage, an ensemble was formed by four filter methods-Mutual Information, ReliefF, Chi Square, and Xvariance-and then each feature from the union set was assessed by three classification algorithms-support vector machine, naive Bayes, and k-nearest neighbors-and an average accuracy was calculated. The features with higher accuracy were selected to obtain a preliminary subset of optimal features. In the second stage, Pearson correlation was used to discard highly correlated features. In these two stages, XGBoost classification algorithm was applied to obtain the most contributing features that, in turn, provide the best optimal subset. Then, in the final stage, we fed the obtained feature subset to a meta-heuristic algorithm, called whale optimization algorithm, in order to further reduce the feature set and to achieve higher accuracy. We evaluated the proposed feature selection framework on four publicly available disease datasets taken from the UCI machine learning repository, namely, arrhythmia, leukemia, DLBCL, and prostate cancer. Our obtained results confirm that the proposed method can perform better than many state-of-the-art methods and can detect important features as well.

关键词： feature selection filter method wrapper method whale optimization algorithm arrhythmia disease classification cancer dataset

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Aspect-based sentiment classification model employing whale-optimized adaptive neural network

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BULLETIN OF THE POLISH ACADEMY OF SCIENCES-TECHNICAL SCIENCES 2021年第3期69卷

作者： Balaganesh, Nallathambi Muneeswaran, K. Mepco Schlenk Engn Coll Autonomous Dept Comp Sci & Engn Sivakasi Tamil Nadu India

Nowadays in e-commerce applications, aspect-based sentiment analysis has become vital, and every consumer started focusing on various aspects of the product before making the purchasing decision on online portals like Amazon, Walmart, Alibaba, etc. Hence, the enhancement of sentiment classification considering every aspect of products and services is in the limelight. In this proposed research, an aspect-based sentiment classification model has been developed employing sentiment whale-optimized adaptive neural network (SWOANN) for classifying the sentiment for key aspects of products and services. The accuracy of sentiment classification of the product and services has been improved by the optimal selection of weights of neurons in the proposed model. The promising results are obtained by analyzing the mobile phone review dataset when compared with other existing sentiment classification approaches such as support vector machine (SVM) and artificial neural network (ANN). The proposed work uses key features such as the positive opinion score, negative opinion score, and term frequency-inverse document frequency (TF-IDF) for representing each aspect of products and services, which further improves the overall effectiveness of the classifier. The proposed model can be compatible with any sentiment classification problem of products and services.

关键词： aspect-based sentiment analysis whale optimization algorithm artificial neural network opinion mining

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Predicting of Runoff Using an Optimized SWAT-ANN: A Case Study

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JOURNAL OF HYDROLOGY-REGIONAL STUDIES 2020年 29卷

作者： LV, Zhihua Zuo, Junjie Rodriguez, Dragan Shanghai Landscape Architecture Design & Res Inst Shanghai 200031 Peoples R China Jiangsu Univ Sch Art Dept Environm Design Zhenjiang 212000 Jiangsu Peoples R China Shanghai Engn Res Ctr Landscape Water Environm Shanghai 200031 Peoples R China Case Western Reserve Univ Dept Elect Engn & Comp Sci Cleveland OH 44106 USA

Study region: The Baliqlu Chai Watershed is located in Iran in Ardabil province, with area of 1036 km2 and average rainfall of 280.9 mm. The area has been plagued with mismanagement of water resources. Due to the degradation of vegetation, runoff increases, that it can cause problems. Thus, forecasting runoff over the next few years can help manage water resources. Study focus: In this study, used of the SWAT and ANN models and an improved model of a metaheuristic employed for resolving the explained shortcoming. The method of optimization is based on a Mutated model of the whale optimization algorithm (MWOA) that enhances the expected results by reducing the error in the ANN. First, the runoff estimated by the SWAT model, for this purpose, is used of 30 years of statistical data for the calibration and validation model. simulated runoff transmitted as input to the ANN and evaluated, and the MLP/MWOA algorithm used to improve the accuracy of the predicted runoff. New hydrological insights: The results show that according to the regression model, the points distribution in SWAT-MLP/MWOA model is less and has the best linear fit. The values obtained from statistical indices showed that the SWAT-ANN is better than the SWAT model because it has R2=0.80, RMSE=1.61, NSE=0.79, RE=-0.11. But SWAT-MLP/WOA model has R2=0.84, RMSE=1.42, NSE=0.81 and RE=-0.09, so SWAT-MLP/MWOA model is presented as the best model for runoff prediction.

关键词： Runoff forecasting model calibration artificial neural network whale optimization algorithm Mutation watershed soil and water assessment tool

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A new sizing and optimization framework for stand-alone hybrid renewable energy systems

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2019年第3期37卷 4043-4053页

作者： Xia, Yaowei Qin, Jiejie Hubei Univ Technol Sch Elect & Elect Engn Wuhan 430000 Hubei Peoples R China

In this paper, a new optimization methodology to assess the designs of the various renewable generation systems of electrical energy is used. This methodology utilizes whale optimization algorithm (WOA) to minimize the cost of the electrical energy generated. The methodology permits to examine and to combine different sources of energy as to touch base at an optimal configuration of the hybrid system. This system is capable of providing energy to the predefined site in an achievable way as indicated by certain specialized and financial criteria. The system incorporates wind generation, photovoltaic generation and batteries for energy storage. The recreation results have been acquired with the help of MATLAB programming. Moreover, the outcomes of the proposed methodology have been compared with Particle Swarm optimization (PSO) algorithm for validation. The recreation results demonstrated the predominance of the proposed methodology.

关键词： Hybrid renewable energy system whale optimization algorithm optimization photovoltaic wind off-grid

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