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Meta-heuristic inspired by the behavior of the humpback whale tuned by a fuzzy inference system

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020年第5期39卷 7993-8000页

作者： Krainski Ferrari, Allan Christian Coelho, Leandro dos Santos Leandro, Gideon Villar Osinski, Cristiano Gouvea da Silva, Carlos Alexandre Ctr Univ UniSociesc Curitiba Parana Brazil Univ Fed Parana Dept Elect Engn Elect Engn Grad Program Curitiba Parana Brazil Pontificia Univ Catolica Parana Ind & Syst Engn Grad Program Curitiba Parana Brazil

The whale optimization algorithm (WOA) is a recent meta-heuristic that can be explored in global optimization problems. This paper proposes a new parameter adjustment mechanism that influences the probability of the food recognition process in the whale algorithm. The adjustment is performed using a fuzzy inference system that uses the current iteration number as input information. Our simulation results are compared with other meta-heuristics such as the conventional version of WOA, Particle Swarm optimization (PSO) and Differential Evolution (DE). All algorithms are used to optimize ten test functions (Sphere, Schwefel 2.22, Quartic, Rosenbrock, Ackley, Rastrigin, Penalty 1, Schwefel 2.21, Six hump camel back and Shekel 1) in order to obtain their respective optimal values for be used as criteria for analysis and comparison. The results of the simulations show that the proposed fuzzy inference system improves the convergence of WOA and also is competitive in relation to the other algorithms, i.e., classical WOA, PSO and DE.

关键词： Benchmark functions fuzzy system global optimization meta-heuristics optimization whale optimization algorithm

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Design of fuzzy logic system framework using evolutionary techniques

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SOFT COMPUTING 2020年第6期24卷 4455-4468页

作者： Singh, Sarabjeet Singh, Satvir Banga, Vijay Kumar 1 K Gujral Punjab Tech Univ Kapurthala PB India Amritsar Coll Engn & Technol Amritsar Punjab India

Designing fuzzy logic system is one of the most popular and research-demanding NP-hard problems. It involves numerous parameters like shape and location of fuzzy sets, antecedents and consequents of fuzzy rule base and other strategic parameters like aggregation, implication and defuzzification methods. Time series forecasting has also become increasingly popular for the applications like share market prediction, weather forecasting. Many researchers have investigated the use of fuzzy logic system for forecasting of time series. In this paper, the authors have investigated the design framework of fuzzy logic systems for forecasting benchmark Mackey-Glass time series. Designing fuzzy logic systems is a class of NP-hard problems which is evolved using most popular and recent evolutionary algorithms. Authors have evolved fuzzy logic system using genetic algorithm, particle swarm optimization, artificial bee colony optimization, firefly algorithm and whale optimization algorithm. Finally, from simulations, it is found that whale optimization algorithm requires less time and shows fuzzy system predictions are more precise than others.

关键词： Fuzzy logic system Time series forecasting Genetic algorithm Hybrid particle swarm optimization Firefly algorithm Artificial bee colony optimization whale optimization algorithm

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A hybrid air pollutant concentration prediction model combining secondary decomposition and sequence reconstruction

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ENVIRONMENTAL POLLUTION 2020年第Part3期266卷 115216-115216页

作者： Sun, Wei Huang, Chenchen North China Elect Power Univ Dept Econ Management Baoding 071000 Peoples R China

Acid rain is a serious threat to terrestrial ecosystems. To provide more accurate early warning information for acid rain prevention, urban planning, and travel planning, a novel air pollutant prediction model was proposed in this paper to predict NO2 and SO2. First, the data were decomposed into several sub-sequences by a complete ensemble empirical mode decomposition with adaptive noise. Second, the subsequences are reconstructed by variational mode decomposition and sample entropy. Then, the new subsequences are predicted by the extreme learning machine combined with the whale optimization algorithm. The empirical analysis was carried out through 8 data sets. According to the experimental results, three main conclusions can be drawn. First, the proposed model in this paper has excellent prediction performance and robustness. In all the comparison experiments, the R-2 and RMSE of the proposed model are the best among all the models. Second, data preprocessing is very necessary. After adding the decomposition algorithm, the average improvement levels of R-2 and RMSE were 897.57% and 50.78%, respectively. Third, the re-decomposition of IMF1 is an effective method to improve prediction accuracy. After the re-decomposition of IMF1, R-2 can be improved by 13.64% on average on the original basis, and RMSE can be reduced by 31.99% on average. The results of this study can provide a valuable reference for the research of air pollutant prediction. In future work, the application of the proposed model in other air pollutants or other regions can be explored. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Air pollutant prediction Extreme learning machine whale optimization algorithm Sample entropy Complete ensemble empirical mode decomposition with adaptive noise Acid rain

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Optimal variable structure control with sliding modes for unstable processes

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Journal of Central South University 2021年第10期28卷 3147-3158页

作者： KUMAR Satyendra AJMERI Moina Department of Electrical Engineering National Institute of TechnologyPatna 800005India

In this work,a variable structure control(VSC)technique is proposed to achieve satisfactory robustness for unstable *** values of unknown parameters of VSC are obtained using whale optimization algorithm which was recently reported in *** analysis has been done to verify the suitability of the proposed structure for industrial *** proposed control strategy is applied to three different types of unstable processes including non-minimum phase and nonlinear systems.A comparative study ensures that the proposed scheme gives superior performance over the recently reported VSC ***,the proposed method gives satisfactory results for a cart inverted pendulum system in the presence of external disturbance and noise.

关键词： variable structure control sliding mode control whale optimization algorithm robustness non-linear system

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Hybrid support vector machines with heuristic algorithms for prediction of daily diffuse solar radiation in air-polluted regions

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RENEWABLE ENERGY 2020年 145卷 2034-2045页

作者： Fan, Junliang Wu, Lifeng Ma, Xin Zhou, Hanmi Zhang, Fucang Northwest A&F Univ Key Lab Agr Soil & Water Engn Arid & Semiarid Are Minist Educ Yangling 712100 Shaanxi Peoples R China Southwest Univ Sci & Technol Sch Sci Mianyang 621010 Sichuan Peoples R China Henan Univ Sci & Technol Coll Agr Engn Luoyang 471003 Peoples R China

Increasing air pollutants significantly affect the proportion of diffuse (R-d) to global (R-s) solar radiation. This study proposed three new hybrid support vector machines (SVM) with particle swarm optimization algorithm (SVM-PSO), bat algorithm (SVM-BAT) and whale optimization algorithm (SVM-WOA) for predicting daily R-d in air-polluted regions. These models were further compared to standalone SVM, multivariate adaptive regression spline (MARS) and extreme gradient boosting (XGBoost) models. The results showed that models with suspended particulate matter with aerodynamic diameter smaller than 2.5 mu m and 10 mu m (PM2.5 and PM10) and ozone (O-3) produced more accurate daily R-d estimates than those without air pollution parameters, with average relative decreases in root mean square deviation (RMSD) of 11.1%, 10.0% and 10.4% for sunshine duration-based, R-s-based and combined models, respectively. SVM showed better accuracy than XGBoost and MARS. However, compared to SVM, SVM-BAT further enhanced the prediction accuracy and convergence rate in daily R-d modeling, followed by SVM-WOA and SVM-PSO, with relative decreases in RMSD of 2.9%-5.6%, 1.9%-4.9% and 1.1%-3.3%, respectively. The results highlighted the significance of incorporating air pollutants for more accurate estimation of daily R-d in air-polluted regions. Heuristic algorithms, especially BAT, are highly recommended for improving performance of standalone machine learning models. (c) 2019 Elsevier Ltd. All rights reserved.

关键词： Air pollution Support vector machines Extreme gradient boosting Particle swarm optimization algorithm Bat algorithm whale optimization algorithm

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Enhancement of low voltage ride-through ability of the photovoltaic array aided by the MPPT algorithm connected with wind turbine

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DATA TECHNOLOGIES AND APPLICATIONS 2020年第4期54卷 503-527页

作者： Madan, Charanjeet Kumar, Naresh DCR Univ Sci & Technol Dept Elect Engn Sonipat India

Purpose By means of the massive environmental and financial reimbursements, wind turbine (WT) has turned out to be a satisfactory substitute for the production of electricity by nuclear or fossil power plants. Numerous research studies are nowadays concerning the scheme to develop the performance of the WT into a doubly fed induction generator-low voltage ride-through (DFIG-LVRT) system, with utmost gain and flexibility. To overcome the nonlinear characteristics of WT, a photovoltaic (PV) array is included along with the WT to enhance the system's performance. Design/methodology/approach This paper intends to simulate the control system (CS) for the DFIG-LVRT system with PV array operated by the MPPT algorithm and the WT that plays a major role in the simulation of controllers to rectify the error signals. This paper implements a novel method called self-adaptive whale with fuzzified error (SWFE) design to simulate the optimized CS. In addition, it distinguishes the SWFE-based LVRT system with standard LVRT system and the system with minimum and maximum constant gain. Findings Through the performance analysis, the value of gain with respect to the number of iterations, it was noted that at 20th iteration, the implemented method was 45.23% better than genetic algorithm (GA), 50% better than particle swarm optimization (PSO), 2.3% better than ant bee colony (ABC) and 28.5% better than gray wolf optimization (GWO) techniques. The investigational analysis has authenticated that the implemented SWFE-dependent CS was effectual for DFIG-LVRT, when distinguished with the aforementioned techniques. Originality/value This paper presents a technique for simulating the CS for DFIG-LVRT system using the SWFE algorithm. This is the first work that utilizes SWFE-based optimization for simulating the CS for the DFIG-LVRT system with PV array and WT.

关键词： LVRT Doubly fed induction generators (DFIGs) Wind turbine PV array Maximum power point tracking whale optimization algorithm

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A Fault Classification Method for Rolling Bearing Based on Multisynchrosqueezing Transform and WOA-SMM

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IEEE ACCESS 2020年 8卷 215355-215364页

作者： Zheng, Jinde Gu, Mingen Pan, Haiyang Tong, Jinyu Anhui Univ Technol Sch Mech Engn Maanshan 243032 Peoples R China

To solve the problem that the limited time-frequency features cannot fully represent the deep-seated state information of rolling bearing, the time-frequency analysis method, whale optimization algorithm (WOA) and support matrix machine (SMM) are combined, and a fault diagnosis model based on multisynchrosqueezing transform (MSST) and WOA-SMM is proposed. First, the time-frequency trait of the original signal is extracted by MSST. Then, using the time-frequency spectrum processed by MSST as the input of SMM, MSST solves the problem of state information loss when constructing a characteristic matrix. Finally, the parameters of the SMM are optimized by WOA and the ideal parameters can be obtained adaptively, and the problem of setting parameters subjectively is solved. The experimental analysis of the two datasets shows that WOA-SMM is superior not only to other classifiers in classification performance, but also has higher in convergence accuracy and speed for rolling bearing fault diagnosis.

关键词： Time-frequency analysis Fault diagnosis optimization whales Rolling bearings Transforms Feature extraction WOA-SMM whale optimization algorithm fault diagnosis rolling bearing multisynchrosqueezing transform

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Research on New Adaptive whale algorithm

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IEEE ACCESS 2020年 8卷 90165-90201页

作者： Chen, Xuan Zhejiang Ind Polytech Coll Design & Art Shaoxing 312000 Peoples R China

Bionic algorithms have always played an important role in industrial, agricultural, and scientific research. The optimization of bionics algorithms has always been the focus of scholars in various countries. A whale algorithm based on optimization based on adaptive convergence and Levy features (IWOA) is proposed to overcome the disadvantages, such as low precision, slow convergence speed and tending to involve the local optimum of the whale algorithm. The improved Bernouilli Shift map is used to initialize the population to maintain diversity of the population. Optimizing the adaptive convergence factor is able to balance the local and global optimization ability. The Levy flight mechanism is introduced to optimize foraging behavior and improve global searching ability. In addition, the trigger rule is applied to screen individuals after each iteration to maintain individual vitality and enhance overall performance of the algorithm. In the simulation, IWOA, Ant Colony optimization, Particle Swarm optimization, whale optimization algorithm and the optimized whale algorithms CWOA, LWOA are compared using the 20 classical test functions. The simulation results demonstrate that the IWOA algorithm possesses good global and local searching ability, especially in solving multi-peak and high-dimensional functions.

关键词： optimization whales Convergence Sociology Statistics Search problems Prediction algorithms whale optimization algorithm convergence factor Levy behavior triggering rule

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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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Multi-target QSAR modelling of chemo-genomic data analysis based on Extreme Learning Machine

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KNOWLEDGE-BASED SYSTEMS 2020年 188卷 104977-104977页

作者： Anter, Ahmed M. Moemen, Yasmine S. Darwish, Ashraf Hassanien, Aboul Ella Shenzhen Univ Hlth Sci Ctr Sch Biomed Engn Shenzhen 518060 Peoples R China Beni Suef Univ Fac Comp & Informat Bani Suwayf 62511 Egypt Menoufia Univ Natl Liver Inst Clin Pathol Dept Menoufia Egypt Helwan Univ Fac Sci Cairo Egypt Cairo Univ Fac Comp & Informat Cairo Egypt SAGE Cairo Egypt

This paper presents a new Quantitative Structure-Activity Relationship (QSAR) model based on Extreme Learning Machine (ELM) to predict the biological activity of the benchmark Escape-Data sets compounds in order to provide an effective learning solution for regression analysis. The pre-processing phase of this model has been performed for the chemo-genomics datasets using the k-Nearest Neighbours (k-NN) algorithm to predict missing values of the dataset. In the second phase, the Genetic algorithm hybrid with Binary whale optimization algorithm (GBWOA) is adapted to determine the significance and the optimized features in feature selection phase. The minmax method is used in the third phase to transform all features to binary form in order to increases the efficiency of the proposed model by smoothing the data points and reducing fluctuation among features. ELM is used in the final phase as regression algorithm to predict chemo-genomics chemical compound. Different experiments have been performed in this paper on datasetwhich has been collected from ExCAPE chemo-genomics database project composed of 43509 compounds, 1134 targets besides biological activity and 40 chemical descriptors. The experimental results show that the proposed model is efficient in improving the level of prediction based on some statistical measurements. Also, ELM produced satisfactory results when the number of hidden nodes is greater than or equal to 1000 L. Moreover, the proposed model achieved high accuracy using R-2 measure (approximate to 0.971) which outperforms the other algorithms in literature such as (WOA, ALO, BAT and CSA) with accuracies (approximate to 0.673, approximate to 0.753, approximate to 0.680, and approximate to 0.897) respectively. In addition, the docking results succeeded in validating the current QSAR model. In the current research, 41686 (95.81%) compounds are lead compound and 36965 (84.95%) compounds are a candidate for multi-target genes. (C) 2019 Elsevier B.V. All

关键词： Machine learning whale optimization algorithm Genetic algorithm Extreme learning machine QSAR Molecular docking ExCAPE chemo-genomics database

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