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PV Output Power Enhancement using whale optimization algorithm under Normal and Shading Conditions

INTERNATIONAL JOURNAL OF RENEWABLE ENERGY RESEARCH 2020年第3期10卷 1536-1543页

作者： Maniraj, B. Fathima, A. Peer Vellore Inst Technol Sch Elect Engn Chennai 600127 Tamil Nadu India

This paper deals with whale hunting behaviour inspired whale optimization algorithm (WOA) for tracking maximum power from the solar photovoltaic (PV) system. Maximum power point tracking (MPPT) controller has become an essential requirement for tracking the actual power present in solar PV system. In literature, various optimization techniques have been proposed for normal and partial shading conditions (PSC). But the problem in the conventional methods is tracking peak power under shading conditions is not assurance due to the presence of many peaks in a shading conditions. Because the local peaks are presented very close to global peaks. The results of conventional algorithms get failed with local peaks instead of tracking global peak power particularly in shading conditions. In this paper, a new WOA algorithm has been proposed which has the ability to reach the peak power presented in solar PV panel under different climatic conditions. In further, the proposed WOA algorithm has been investigated in MATLAB/Simulink model and comparison has been made with different MPPT algorithms, namely, Perturb and Observe (PO), grey wolf optimization (GWO). The results clearly demonstrated the proposed WOA algorithm giving more than 99.6% efficiency with high tracking speed and minimum payback period under PSC.

关键词： Maximum power point tracking Photovoltaic Partial shading conditions whale optimization algorithm

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An intelligent lung tumor diagnosis system using whale optimization algorithm and support vector machine

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2020年第2期11卷 374-384页

作者： Vijh, Surbhi Gaur, Deepak Kumar, Sushil Amity Univ Dept Comp Sci & Engn Sect 125 Noida Uttar Pradesh India Natl Inst Technol Warangal Dept Comp Sci & Engn Warangal Telangana India

Medical image processing technique are widely used for detection of tumor to increase the survival rate of patients. The development of computer-aided diagnosis system shows improvement in observing the medical image and determining the treatment stages. The earlier detection of tumor reduces the mortality of lung cancer by increasing the probability of successful treatment. In this paper, the intelligent lung tumor diagnosis system is developed using various image processing technique. The simulated steps involve image enhancement, image segmentation, post-processing, feature extraction, feature selection and classification using support vector machine (SVM) kernel. Gray level co-occurrence matrix method is used for extracting the 19 texture and statistical features of lung computed tomography (CT) image. whale optimization algorithm (WOA) is considered for selection of best prominent feature subset. The contribution provided in this paper is the development of WOA_SVM to automate the aided diagnosis system for determining whether the lung CT image is normal or abnormal. An improved technique is developed using whale optimization algorithm for optimal feature selection to obtain accurate results and constructing the robust model. The performance of proposed methodology is evaluated using accuracy, sensitivity and specificity and obtained as 95%, 100% and 92% using radial bias function support vector kernel.

关键词： Lung tumor Global thresholding Gray level co-occurrence matrix whale optimization algorithm Support vector machine

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On Some Improved Versions of whale optimization algorithm

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2019年第11期44卷 9653-9691页

作者： Salgotra, Rohit Singh, Urvinder Saha, Sriparna Thapar Inst Engn & Technol Dept ECE Patiala Punjab India Indian Inst Technol Dept CSE Patna Bihar India

whale optimization algorithm (WOA) is a recently developed swarm intelligence-based algorithm which is inspired from the social behavior of humpback whale. This algorithm mimics the bubble-net hunting strategy of whales and has been applied to optimization problems. But the algorithm suffers from the problem of poor exploration and local optima stagnation. In this paper, three different modified algorithms of WOA have been proposed to improve its explorative ability. The modified versions are based on the concepts of opposition-based learning, exponentially decreasing parameters and elimination or re-initialization of worst particles. These properties have been added to improve the explorative properties of WOA by maintaining diversity among the search agents. The proposed algorithms have been tested on CEC2005 benchmark problems for variable population and dimension sizes. Statistical testing and scalability testing of the best algorithm have been carried out to prove its significance over other algorithms such as with well-known algorithms such as bat algorithm, bat flower pollinator, differential evolution, firefly algorithm, flower pollination algorithm. It has been found from the experimental results that the performance of all the proposed versions is better than the original WOA. Here, opposition- and exponential-based WOA is the best among all the proposed variants. Statistical testing and convergence profiles further validate the results.

关键词： whale optimization algorithm Numerical optimization Benchmark problems Meta-heuristic algorithms Differential evolution

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An image denoising method based on BP neural network optimized by improved whale optimization algorithm

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EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING 2021年第1期2021卷 1-22页

作者： Wang, Chunzhi Li, Min Wang, Ruoxi Yu, Han Wang, Shuping Hubei Univ Technol Sch Comp Sci Wuhan Peoples R China Wuhan Fiberhome Tech Serv Co Ltd Wuhan Peoples R China Fiberhome Telecommun Technol Co Ltd Wuhan Peoples R China Huazhong Univ Sci & Technol Hubei Canc Hosp Tongji Med Coll Dept Informat Stat Wuhan Peoples R China

As an important part of smart city construction, traffic image denoising has been studied widely. Image denoising technique can enhance the performance of segmentation and recognition model and improve the accuracy of segmentation and recognition results. However, due to the different types of noise and the degree of noise pollution, the traditional image denoising methods generally have some problems, such as blurred edges and details, loss of image information. This paper presents an image denoising method based on BP neural network optimized by improved whale optimization algorithm. Firstly, the nonlinear convergence factor and adaptive weight coefficient are introduced into the algorithm to improve the optimization ability and convergence characteristics of the standard whale optimization algorithm. Then, the improved whale optimization algorithm is used to optimize the initial weight and threshold value of BP neural network to overcome the dependence in the construction process, and shorten the training time of the neural network. Finally, the optimized BP neural network is applied to benchmark image denoising and traffic image denoising. The experimental results show that compared with the traditional denoising methods such as Median filtering, Neighborhood average filtering and Wiener filtering, the proposed method has better performance in peak signal-to-noise ratio.

关键词： Image denoising BP neural network whale optimization algorithm Nonlinear convergence factor Adaptive weight coefficient

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whale optimization algorithm-based point cloud data processing method for sewer pipeline inspection

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AUTOMATION IN CONSTRUCTION 2022年 141卷

作者： Liu, Wenli Shao, Yixiao Chen, Ke Li, Chen Luo, Hanbin Huazhong Univ Sci & Technol Sch Civil & Hydraul Engn Dept Construct Management Wuhan 430074 Peoples R China

Sewerages are critical infrastructure assets for wastewater carriage in urban lifelines, but their function can be seriously affected by blockages or deterioration. Existing sewer pipeline inspection methods, such as closed-circuit television and sonar detection, have been blamed for low efficiency and considerable noise in the collected data. Therefore, this paper attempts to enhance the blockage and deterioration assessment inside the sewer pipeline by proposing a whale optimization algorithm-based point cloud data (WOAPCD) processing method. The method consists of an improved WOA for data clustering and fitting and a reverse slicing method for modeling the as-is conditions. The applicability of this proposed approach is validated in an actual sewerage system, and the results show that the WOAPCD can accurately and effectively reconstruct the 3D model of the sewer, providing valuable information for quantifying siltation conditions. The proposed method has better performance than PSO and GA in terms of the fitting error and modeling speed.

关键词： Sewer pipeline inspection Point cloud data whale optimization algorithm 3D model reconstruction

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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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A New Chaotic whale optimization algorithm for Features Selection

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JOURNAL OF CLASSIFICATION 2018年第2期35卷 300-344页

作者： Sayed, Gehad Ismail Darwish, Ashraf Hassanien, Aboul Ella Cairo Univ Fac Comp & Informat 5 Ahmed Zewail Ad Doqi 12613 Giza Governorat Egypt Helwan Univ Fac Sci Helwan Egypt

The whale optimization algorithm (WOA) is a novel evolutionary algorithm inspired by the behavior of whales. Similar to other evolutionary algorithms, entrapment in local optima and slow convergence speed are two probable problems it encounters in solving challenging real applications. This paper presents a novel chaotic whale optimization algorithm (CWOA) to overcome these problems where chaotic search is embedded in the searching iterations of WOA. Ten chaotic maps are considered to improve the performance of WOA. Experiments on ten benchmark datasets show the novel CWOA is effective for selecting relevant features with a high classification performance and a small number of features. Additionally the performance of CWOA is compared with WOA and ten other optimization algorithms. The experimental results show that circle chaotic map is the best chaotic map to significantly boost the performance of WOA. Moreover, chaotic with modifications of exploration operators outperform the highest performance.

关键词： whale optimization algorithm Chaotic maps Features selection

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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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Binary whale optimization algorithm and Binary Moth Flame optimization with Clustering algorithms for Clinical Breast Cancer Diagnoses

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JOURNAL OF CLASSIFICATION 2020年第1期37卷 66-96页

作者： Sayed, Gehad Ismail Darwish, Ashraf Hassanien, Aboul Ella Cairo Univ Fac Comp & Informat Egypt Sci Res Grp Egypt SRGE Cairo Egypt Helwan Univ Sci Res Grp Egypt SRGE Fac Sci Cairo Egypt

Models based on machine learning algorithms have been developed to detect the breast cancer disease early. Feature selection is commonly applied to improve the performance of these models through selecting only relevant features. However, selecting relevant features in unsupervised learning is much difficult. This is due to the absence of class labels that guide the search for relevant information. This kind of the problem has rarely been studied in the literature. This paper presents a hybrid intelligence model that uses the cluster analysis algorithms with bio-inspired algorithms as feature selection for analyzing clinical breast cancer data. A binary version of both moth flame optimization and whale optimization algorithm is proposed. Two evaluation criteria are adopted to evaluate the proposed algorithms: clustering-based measurements and statistics-based measurements. The experimental results positively demonstrate that the capability of the proposed bio-inspired feature selection algorithms to produce both meaningful data partitions and significant feature subsets.

关键词： Intelligent systems Breast cancer Feature selection whale optimization algorithm Moth flame optimization WBCD

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whale optimization algorithm based on Levy flight and memory for static smooth path planning

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INTERNATIONAL JOURNAL OF MODERN PHYSICS C 2022年第10期33卷 2250138-2250138页

作者： Zong, Xinlu Liu, Jiajie Ye, Zhiwei Liu, Yin Hubei Univ Technol Sch Comp Sci Wuhan Peoples R China

In this paper, a whale optimization algorithm based on Levy flight and memory (WOALFM) is proposed to solve the curvature discontinuity problem in the global path planning of unmanned vehicles. Levy flight and chaotic mapping are introduced to disturb the solutions of each generation and enhance the diversity of solutions. A memory strategy based on fractional-order expansion is presented to remember the influence of the positions of individuals in previous generations on the positions of current generation. This strategy based on Levey flight and memory can enhance searching ability and avoid falling into local optimum. Furthermore, the ratio of global searching to local searching can be adjusted to achieve desired results in WOALFM algorithm. The proposed WOALFM algorithm is tested and compared with five algorithms including whale optimization algorithm (WOA), Moth-Flame optimization (MFO), particle swarm optimization (PSO), Fractional-Order Velocity based Particle Swarm optimization (FOPSO) and Grey Wolf Optimizer (GWO) on 23 standard benchmark functions. The experimental results show the effectiveness of WOALFM. The proposed algorithm is applied to smooth path planning problem of unmanned vehicles. Three factors, including the length, curvature and curvature derivative of a path are considered in order to obtain the shortest smooth path without collisions. The experimental results show that more collision-free paths can be obtained in lower computational cost by the presented method.

关键词： Smooth path planning whale optimization algorithm Bezier curve Levy flight

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