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HWOA: A hybrid whale optimization algorithm with a novel local minima avoidance method for multi-level thresholding color image segmentation

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EXPERT SYSTEMS WITH APPLICATIONS 2022年第0期190卷 116145-116145页

作者： Abdel-Basset, Mohamed Mohamed, Reda AbdelAziz, Nabil M. Abouhawwash, Mohamed Zagazig Univ Zagazig 44519 Ash Sharqia Gov Egypt Mansoura Univ Fac Sci Dept Math Mansoura 35516 Egypt Michigan State Univ Dept Computat Math Sci & Engn CMSE E Lansing MI 48824 USA

Traditional methods to address color image segmentation work efficiently for bi-level thresholding. However, for multi-level thresholding, traditional methods suffer from time complexity that increases exponentially with the increasing number of threshold levels. To overcome this problem, in this paper, a new approach is proposed to tackle multi-threshold color image segmentation by employing the Otsu method as an objective function. This approach is based on a hybrid of the whale optimization algorithm (WOA) with a novel method called the local minima avoidance method (LMAM), abbreviated as HWOA. LMAM avoids local minima by updating the whale either within the search space of the problem or between two whales selected randomly from the population-based on a certain probability. HWOA is validated on ten color images taken from the Berkeley University Dataset by measuring the objective values, peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), features similarity index (FSIM), and CPU time, and compared with a number of the well-known robust meta-heuristic algorithms: the sine-cosine algorithm (SCA), WOA, modified salp swarm algorithm (MSSA), improved marine predators algorithm (IMPA), modified Cuckoo Search (CS) using McCulloch's algorithm (CSMC), and equilibrium optimizer (EO). The experimental results show that HWOA is superior to all the other algorithms in terms of PSNR, FSIM, and objective values, and is competitive in terms of SSIM.

关键词： Color image segmentation whale optimization algorithm Otsu method Local minima elimination method Multi-level thresholding

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Application of Variational Mode Decomposition and whale optimization algorithm to Laser Ultrasonic Signal Denoising

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SENSORS 2023年第1期23卷 354页

作者： Mao, Xing Yang, Quan Wang, Xiaocheng Li, Jingdong Univ Sci & Technol Beijing Inst Engn Technol Beijing 100083 Peoples R China

Laser ultrasound signal echoes are easily drowned out by the surrounding environmental noise in industrial field applications, and it is worthwhile to study methods of retaining the weak ultrasound signal during signal processing. To address this problem, this paper proposes to adopt the parameters optimized by the whale optimization algorithm to the variational mode decomposition (VMD) of laser ultrasound signals. The optimized parameters can avoid the frequency mixing and incomplete noise separation caused by the choice of artificial VMD parameters. The Hausdorff distance is applied in the process of reconstructing the signal to help accurately select the relevant modes and improve the signal-to-noise ratio. Simulation and experimental results show that the proposed method is feasible and effective compared with the other three available denoising methods.

关键词： laser ultrasonic signal denoising variational mode decomposition Hausdorff distance whale optimization algorithm

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Research on Parameter Inversion of Coal Mining Subsidence Prediction Model Based on Improved whale optimization algorithm

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ENERGIES 2024年第5期17卷 1158页

作者： Guo, Qingbiao Qiao, Boqing Yang, Yingming Guo, Junting State Key Lab Water Resource Protect & Utilizat Co Beijing 100000 Peoples R China Anhui Univ Sci & Technol Sch Spatial Informat & Geomat Engn Huainan 232001 Peoples R China

Rapid coal mining results in a series of mining subsidence damages. Predicting surface movement and deformation accurately is essential to reducing mining damage. The accurate determination of parameters for a mining subsidence prediction model is crucial for accurately predicting mining subsidence. In this research, with the incorporation of the Sobol sequence and Levy flight strategy, we propose an improved whale optimization algorithm (IWOA), thereby enhancing its global optimization capability and mitigating local optimization issues. Our simulation experiment results demonstrate that the IWOA achieved a root mean square error and relative error of less than 0.42 and 0.27%, respectively, indicating its superior accuracy compared to a basic algorithm. The IWOA inversion model also exhibits superior performance compared to a basic algorithm in mitigating gross error interference, Gaussian noise interference, and missing observation point interference. Additionally, it demonstrates enhanced global search capabilities. The IWOA was employed to perform parameter inversion for the working face 1414(1) in Guqiao Coal Mine. The root mean square error of the inversion results did not exceed 6.03, while the subsidence coefficient q, tangent of the main influence angle tan beta, horizontal movement coefficient b, and mining influence propagation angle theta were all below 0.32. The average value of the fitted root mean square error for the subsidence value's fitted root mean square error and horizontal movement value's fitted root mean square error of the IWOA was 91.51 mm, which satisfies the accuracy requirements for general engineering applications.

关键词： mining subsidence whale optimization algorithm Sobol sequence Levy flight probability integral method parameter inversion

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Short-term traffic flow prediction based on whale optimization algorithm optimized BiLSTM_Attention

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2022年第10期34卷 e6782-e6782页

作者： Xu, Xing Liu, Chengxing Zhao, Yun Lv, Xiaoshu Zhejiang Univ Sci & Technol Sch Mech & Energy Engn Hangzhou Peoples R China Zhejiang Univ Sci & Technol Sch Informat & Elect Engn Hangzhou Peoples R China Univ Vaasa Dept Elect Engn & Energy Technol Vaasa Finland Aalto Univ Dept Civil Engn Espoo Finland

With the growths in population and vehicles, traffic flow becomes more complex and uncertain disruptions occur more often. Accurate prediction of urban traffic flow is important for intelligent decision-making and warning, however, remains a challenge. Many researchers have applied neural network methods, such as convolutional neural networks and recurrent neural networks, for traffic flow prediction modeling, but training the conventional network that can obtain the best network parameters and structure is difficult, different hyperparameters lead to different network structures. Therefore, this article proposes a traffic flow prediction model based on the whale optimization algorithm (WOA) optimized BiLSTM_Attention structure to solve this problem. The traffic flow is predicted first using the BiLSTM_Attention network which is then optimized by using the WOA to obtain its four best parameters, including the learning rate, the training times, and the numbers of the nodes of two hidden layers. Finally, the four best parameters are used to build a WOA_BiLSTM_Attention model. The proposed model is compared with both conventional neural network model and neural network model optimized by the WOA. Based on the evaluation metrics of MAPE, RMSE, MAE, and R2, the WOA_BiLSTM_Attention model proposed in this article presents the best performance.

关键词： attention BiLSTM prediction traffic flow whale optimization algorithm

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An urban short-term traffic flow prediction model based on wavelet neural network with improved whale optimization algorithm

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SUSTAINABLE CITIES AND SOCIETY 2021年 69卷 102858-102858页

作者： Du, Wangdi Zhang, Qingyong Chen, Yuepeng Ye, Ziliu Wuhan Univ Technol Sch Automat Wuhan Hubei Peoples R China Wuhan Univ Technol Wuhan Hubei Peoples R China

To solve the problem of urban traffic congestion is a major challenge to modern society. Short-term urban traffic flow prediction is the key to realizing traffic control and vehicle guidance on the relief of traffic congestion to make a better city experience. Short-term urban traffic flow is characterized by a high level of time-variation, nonlinearity and randomness. To tackle this problem and to improve the performance of short-term traffic flow prediction, the author of this paper comes up with an improved whale optimization algorithm (IWOA) and tries to introduce it into the wavelet neural network (WNN) so as to optimize its initial weights and wavelet factors via iteration and to establish a short-term traffic flow prediction model based on Wavelet Neural Network with Improved whale optimization algorithm (IWOA-WNN). This model appears to predict traffic flow in a more effective manner and it can remedy the defects of wavelet neural network which usually leads to low prediction accuracy and slow response. The experimental results show that compared with the pure WNN model and WOAWNN model, the IWOA-WNN model has turned out to be a great improvement in terms of absolute error and percentage of absolute error.

关键词： Short-term traffic flow prediction whale optimization algorithm Wavelet neural network

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Source term inversion coupling Kernel Principal Component Analysis, whale optimization algorithm, and Backpropagation Neural Networks (KPCA-WOA-BPNN) for complex dispersion scenarios

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PROGRESS IN NUCLEAR ENERGY 2024年 171卷

作者： Li, Xinpeng Song, Jiayue Yang, Li Li, Huanting Fang, Sheng North China Elect Power Univ Sch Nucl Sci & Engn Beijing 102206 Peoples R China Tsinghua Univ Inst Nucl & New Energy Technol Collaborat Innovat Ctr Adv Nucl Energy Technol Key Lab Adv Reactor Engn & SafetyMinist Educ Beijing 100084 Peoples R China

Accurate and rapid source term estimation is critical for consequence assessment and emergency decisionmaking in nuclear accidents. Neural network methods provide a promising approach to achieving this goal, but they are mainly demonstrated in ideal scenarios with flat terrain. In this study, a source term inversion model combining the Kernel Principal Component Analysis, the whale optimization algorithm, and the Backpropagation Neural Networks (KPCA-WOA-BPNN) was proposed for source term inversion in a more realistic scenario with complex terrain. The method was validated against two simulated release scenarios based on the meteorology and topography of a real Chinese nuclear power plant. A comprehensive sensitivity analysis of the position distribution, quantity, and quality of measurement sites and the four input parameters was conducted. The results revealed that the relative error of the KPCA-WOA-BPNN was below 2% for both scenarios. The sites within 1.2 km of the release point are critical for the performance. Even with only 6 sites, the error of the method can be as low as 3.82%. The method is insensitive to the disturbances in wind speed, mix layer height, and release height, but less robust to those in concentration. The method also works with other optimization algorithms and presents low error levels, indicating good extendibility.

关键词： Source term inversion Kernel principal component analysis whale optimization algorithm Backpropagation neural network Complex dispersion scenarios Wind tunnel experiment

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COVID-19 X-ray image segmentation by modified whale optimization algorithm with population reduction

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COMPUTERS IN BIOLOGY AND MEDICINE 2021年 139卷 104984-104984页

作者： Chakraborty, Sanjoy Saha, Apu Kumar Nama, Sukanta Debnath, Sudhan Natl Inst Technol Dept Comp Sci & Engn Agartala Tripura India Iswar Chandra Vidyasagar Coll Dept Comp Sci & Engn Belonia Tripura India Natl Inst Technol Dept Math Agartala Tripura India Maharaja Bir Bikram Univ Dept Appl Math Agartala Tripura India Maharaja Bir Bikram Univ Dept Chem Agartala Tripura India

Coronavirus disease 2019 (COVID-19) has caused a massive disaster in every human life field, including health, education, economics, and tourism, over the last year and a half. Rapid interpretation of COVID-19 patients' X-ray images is critical for diagnosis and, consequently, treatment of the disease. The major goal of this research is to develop a computational tool that can quickly and accurately determine the severity of an illness using COVID-19 chest X-ray pictures and improve the degree of diagnosis using a modified whale optimization method (WOA). To improve the WOA, a random initialization of the population is integrated during the global search phase. The parameters, coefficient vector (A) and constant value (b), are changed so that the algorithm can explore in the early stages while also exploiting the search space extensively in the latter stages. The efficiency of the proposed modified whale optimization algorithm with population reduction (mWOAPR) method is assessed by using it to segment six benchmark images using multilevel thresholding approach and Kapur's entropy-based fitness function calculated from the 2D histogram of greyscale images. By gathering three distinct COVID-19 chest X-ray images, the projected algorithm (mWOAPR) is utilized to segment the COVID-19 chest X-ray images. In both benchmark pictures and COVID-19 chest X-ray images, comparisons of the evaluated findings with basic and modified forms of metaheuristic algorithms supported the suggested mWOAPR's improved performance.

关键词： whale optimization algorithm Image segmentation Multilevel thresholding Kapur's entropy COVID-19 chest X-ray image

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A novel whale optimization algorithm integrated with Nelder-Mead simplex for multi-objective optimization problems

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KNOWLEDGE-BASED SYSTEMS 2021年 212卷 106619-106619页

作者： Abdel-Basset, Mohamed Mohamed, Reda Mirjalili, Seyedali Zagazig Univ Fac Comp & Informat Zagazig 44519 Egypt Torrens Univ Australia Ctr Artificial Intelligence Res & Optimisat Brisbane Qld 4006 Australia Yonsei Univ YFL Yonsei Frontier Lab Seoul South Korea King Abdulaziz Univ Jeddah Saudi Arabia

Recently, several meta-heuristics and evolutionary algorithms have been proposed for tackling optimization problems. Such methods tend to suffer from degraded performance when solving multi objective optimization problems due to addressing the conflicting goals of finding accurate estimation of Pareto optimal solutions and increasing their distribution across all objectives. In this paper, the whale optimization algorithm (WOA) is improved and extended to solve such multi-objective optimization problems with the purpose of alleviating these drawbacks. The improvements include: (1) modifying the distance control factor of the standard WOA to contain values generated dynamically instead of a fixed one, (2) the trade-off between moving toward the opposite of the best solution and its original values based on a certain probability to prevent stuck into local minima, and (3) accelerating the convergence and coverage using Nelder-Mead method and the Pareto Archived Evolution Strategy (PAES). The proposed algorithm is tested on three benchmark multi-objective test functions (DTLZ, CEC 2009, and GLT), including 25 test functions, to verify its effectiveness by comparing with nine robust multi-objective algorithms. The experiments demonstrate the superiority of the proposed algorithm compared to some of the existing multi-objective algorithms in the literature. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Multi-objective optimization Nelder-Mead method whale optimization algorithm Dynamic distance Opposition based learning optimization algorithm Artificial Intelligence Swarm Intelligence WOA

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WOA-TLBO: whale optimization algorithm with Teaching-learning-based optimization for global optimization and facial emotion recognition

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APPLIED SOFT COMPUTING 2021年 110卷 107623-107623页

作者： Lakshmi, A. Vijaya Mohanaiah, P. JNTUA Dept ECE Anantapur AP India NBKR Inst Sci & Technol Dept ECE Vijayanagar India

The whale optimization algorithm (WOA) is a recently developed algorithm that is based on the chasing mechanism of humpback whales. Benefiting from the unique structure, WOA has virtuous global search capability. One of the drawbacks of this algorithm is the slow convergence rate that limits its real-world application. In resolving complicated global optimization problems, without any exertion for adequate fine-tuning preliminary constraints, Teaching-learning-based optimization (TLBO) is smooth to plunge into local optimal, but it has a fast convergence speed. By given the features of WOA and TLBO, an active hybrid WOA-TLBO algorithm is proposed for resolving optimization difficulties. To explore the enactment of the proposed WOA-TLBO algorithm, several experimentations are accompanied by regular benchmark test functions and compared with six other algorithms. The investigational outcomes indicate the more magnificent concert of the proposed WOA-TLBO algorithm for the benchmark function results. The proposed method has also been applied to the Facial Emotion Recognition (FER) functional problem. FER is the thought-provoking investigation zone that empowers us to classify the expression of the human face in everyday life. Centered on the portions' actions in the human face, the maximum of the standard approaches fail to distinguish the expressions precisely as the expressions. In this paper, we have proposed FER's productive process using WOA-TLBO based MultiSVNN (Multi-Support Vector Neural Network). Investigational outcomes deliver an indication of the virtuous enactment of the proposed technique resolutions in terms of accurateness. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Meta-heuristic whale optimization algorithm WOA-TLBO Facial emotion recognition Population-based algorithm Teaching-learning-based optimization

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HSMA_WOA: A hybrid novel Slime mould algorithm with whale optimization algorithm for tackling the image segmentation problem of chest X-ray images

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APPLIED SOFT COMPUTING 2020年 95卷 106642-106642页

作者： Abdel-Basset, Mohamed Chang, Victor Mohamed, Reda Zagazig Univ Fac Comp & Informat Sharqiyah Egypt Teesside Univ Sch Comp Engn & Digital Technol Middlesbrough Cleveland England

Recently, a novel virus called COVID-19 has pervasive worldwide, starting from China and moving to all the world to eliminate a lot of persons. Many attempts have been experimented to identify the infection with COVID-19. The X-ray images were one of the attempts to detect the influence of COVID-19 on the infected persons from involving those experiments. According to the X-ray analysis, bilateral pulmonary parenchymal ground-glass and consolidative pulmonary opacities can be caused by COVID-19 - sometimes with a rounded morphology and a peripheral lung distribution. But unfortunately, the specification or if the person infected with COVID-19 or not is so hard under the X-ray images. X-ray images could be classified using the machine learning techniques to specify if the person infected severely, mild, or not infected. To improve the classification accuracy of the machine learning, the region of interest within the image that contains the features of COVID-19 must be extracted. This problem is called the image segmentation problem (ISP). Many techniques have been proposed to overcome ISP. The most commonly used technique due to its simplicity, speed, and accuracy are threshold-based segmentation. This paper proposes a new hybrid approach based on the thresholding technique to overcome ISP for COVID-19 chest X-ray images by integrating a novel meta-heuristic algorithm known as a slime mold algorithm (SMA) with the whale optimization algorithm to maximize the Kapur's entropy. The performance of integrated SMA has been evaluated on 12 chest X-ray images with threshold levels up to 30 and compared with five algorithms: Lshade algorithm, whale optimization algorithm (WOA), FireFly algorithm (FFA), Harris-hawks algorithm (HHA), salp swarm algorithms (SSA), and the standard SMA. The experimental results demonstrate that the proposed algorithm outperforms SMA under Kapur's entropy for all the metrics used and the standard SMA could perform better than the other algorithms i

关键词： Image segmentation problem Slime mould algorithm (SMA) whale optimization algorithm Kapur's entropy X-ray images COVID-19

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