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Multi-Strategy Assisted Multi-Objective whale optimization algorithm for Feature Selection

Computer Modeling in Engineering & Sciences 2024年第8期140卷 1563-1593页

作者： Deng Yang Chong Zhou Xuemeng Wei Zhikun Chen Zheng Zhang School of Information Engineering Hebei GEO UniversityShijiazhuang050031China Faculty of Science University of AlbertaEdmontonT2N1N4Canada College of Resources and Environment Beibu Gulf UniversityQinzhou535011China Clinical Laboratory The Frist Hospital of Hebei Medical UniversityShijiazhuang050000China

In classification problems,datasets often contain a large amount of features,but not all of them are relevant for accurate *** fact,irrelevant features may even hinder classification *** selection aims to alleviate this issue by minimizing the number of features in the subset while simultaneously minimizing the classification error ***-objective optimization approaches employ an evaluation function designed as an aggregate function with a parameter,but the results obtained depend on the value of the *** eliminate this parameter’s influence,the problem can be reformulated as a multi-objective optimization *** whale optimization algorithm(WOA)is widely used in optimization problems because of its simplicity and easy *** this paper,we propose a multi-strategy assisted multi-objective WOA(MSMOWOA)to address feature *** enhance the algorithm’s search ability,we integrate multiple strategies such as Levy flight,Grey Wolf Optimizer,and adaptive mutation into ***,we utilize an external repository to store non-dominant solution sets and grid technology is used to maintain *** on fourteen University of California Irvine(UCI)datasets demonstrate that our proposed method effectively removes redundant features and improves classification *** source code can be accessed from the website:https://***/zc0315/MSMOWOA.

关键词： Multi-objective optimization whale optimization algorithm multi-strategy feature selection

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optimization of biodiesel production of spirulina-platensis algae biomass using RSM integrated with whale optimization algorithm

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INDIAN JOURNAL OF ENGINEERING AND MATERIALS SCIENCES 2024年第5期31卷 750-758页

作者： Chhikara, Ashish Dhingra, Ashwani Kumar Chhabra, Deepak Kumar, Pardeep Maharshi Dayanand Univ Univ Inst Engn & Technol Dept Mech Engn Rohtak 124001 Haryana India

Sustainable sources for biodiesel production from non-edible feedstocks are gaining attention due to limited sources of crude petroleum and concern related to the harmful emission of fossil fuels. In this study, Spirulina-Platensis algae biomass has been used for biodiesel production. The effect of methanol to algae biomass ratio, temperature, time, catalyst concentration, and stirring speed for optimization of algae biomass biodiesel using the transesterification process has been studied. The proposed work has implemented the quadratic regression equation obtained by RSM with the whale optimization algorithm. The result shows maximum production of algae biomass biodiesel (89.87%) obtained at the algal biomass/methanol ratio of 5.56, temperature of 54.64 degrees C, time of 78 minutes, H2SO4 of 77.06% w/w, and stirring speed of 450 rpm. The RSM and WOA prediction models have been compared for more. The experimental result shows that WOA optimization can accurately predict the optimal point with a small error. The fundamental properties of algae biomass biodiesel have been checked and compared with fossil diesel.

关键词： whale optimization algorithm RSM Spirulina platensis Transesterification

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Inversion of Bubble Size Distribution Based on whale optimization algorithm

IEEE PHOTONICS JOURNAL

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IEEE PHOTONICS JOURNAL 2024年第4期16卷 1页

作者： Lu, Zhenzhong Hu, Ziqi Ma, Lin Huang, Shangtao Peng, Tao Liu, Min Han, Biao Liao, Jiali Wang, Zihao Sun, Yanling Xidian Univ Sch Optoeletron Engn Xian 710071 Peoples R China

A particle size inversion method based on whale optimization algorithm (WOA) is presented. In the experiment, the small angle forward scattering measurement system is used to conduct experimental research on the inversion of bubble particle size. WOA is used to invert and simulate the bubbles that follow a certain distribution, adding different levels of random noise to verify the stability of the algorithm. The results verify the feasibility and stability of applying WOA to particle size inversion.

关键词： Noise Size measurement whales Particle measurements Atmospheric measurements Search problems Noise level whale optimization algorithm small angle forward scattering method bubble size distribution

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Environmental cold chain distribution center location model in the semiconductor supply chain: A hybrid arithmetic whale optimization algorithm

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COMPUTERS & INDUSTRIAL ENGINEERING 2024年 187卷

作者： Wang, Xiaolin Zhan, Liyi Zhang, Yong Fei, Teng Tseng, Ming-Lang Tianjin Univ Commerce Coll Informat Engn Tianjin Peoples R China Asia Univ Inst Innovat & Circular Econ Taichung Taiwan China Med Univ China Med Univ Hosp Taiwan Dept Med Res Taichung Taiwan Univ Kebangsaan Malaysia Grad Sch Business Bangi 43000 Selangor Malaysia Khon Kaen Univ Dept Ind Engn Dept Khon Kaen Thailand

This study addresses the high cost and high carbon emissions associated with the transportation sector. An environmental cold chain logistics distribution center location model has been designed by considering various constraints such as demand, time windows, and cost factors associated with cargo damage, refrigeration of cold chain cargo, and carbon emissions. An environmental cold chain logistics distribution center location model is designed for the raw materials of low-temperature storage and transportation in the semiconductor supply chain to achieve sustainable development by reducing carbon emissions. This study effectively reduces transportation costs and carbon emissions. To address the issues of slow convergence speed and susceptibility to local optima in the conventional whale optimization algorithm (WOA), a hybrid arithmetic whale optimization algorithm (HAWOA) is proposed by combining arithmetic optimization and the honey badger algorithm. Simulation analysis shows that the HAWOA is capable of obtaining the location scheme with the minimum distribution cost under the same number of iterations, which provides a substantial advantage compared to other algorithms.

关键词： Distribution center location Environmental cold chain Semiconductor supply chain whale optimization algorithm Hybrid arithmetic

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RMOWOA: A Revamped Multi-Objective whale optimization algorithm for Maximizing the Lifetime of a Network in Wireless Sensor Networks

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INTERNATIONAL JOURNAL OF PARALLEL PROGRAMMING 2024年第5-6期52卷 325-366页

作者： Dwivedi, Bhanu Patro, Bachu Dushmanta Kumar Rajkiya Engn Coll REC Dept Comp Sci & Engn Kannauj 209732 India Dr APJ Abdul Kalam Tech Univ AKTU Lucknow 226031 India Manav Rachna Int Inst Res & Studies MRIIRS Sch Engn & Technol Dept Comp Sci & Engn Faridabad 121004 Haryana India

Wireless sensor networks (WSNs) consist of sensor nodes that detect, process, and transmit various types of information to a base station unit. The development of energy-efficient routing protocols is a crucial challenge in WSNs. This study proposes a novel algorithm called RMOWOA, i.e., Revamped Multi-Objective whale optimization algorithm, which utilizes concentric circles with different radii to partition the network. The circles are divided into eight equal sectors, and sections are formed at the intersections of sectors and layers. Each section contains a small number of nodes, and an agent is selected based on specific criteria. The nodes within each section transmit their detected information to the corresponding agent or cluster head. This process is repeated until the base station receives the data. The selection of agents is based on a WOA-based approach, known for enhancing the network's lifetime. The selected agent aggregates the data, performs redundant residue number-based error detection and rectification, and forwards the information to the lower segment's agent within that sector. The proposed RMOWOA algorithm is evaluated through simulation analysis and compared with established benchmark cluster head selection schemes such as SFA- Cluster Head Selection, FCGWO-Cluster Head Selection, and ABC-Cluster Head Selection. The experimental results of the RMOWOA algorithm demonstrate reduced energy consumption and extended network lifespan by effectively balancing the ratio of alive and dead nodes in WSNs.

关键词： Cluster head selection whale optimization algorithm Wireless sensor network Network lifetime

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Swarm intelligence based deep learning model via improved whale optimization algorithm and Bi-directional long short-term memory for fault diagnosis of chemical processes

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ISA TRANSACTIONS 2024年 147卷 227-238页

作者： Ji, Chunlei Zhang, Chu Suo, Leiming Liu, Qianlong Peng, Tian Huaiyin Inst Technol Fac Automat Huaian 223003 Peoples R China Huaiyin Inst Technol Jiangsu Permanent Magnet Motor Engn Res Ctr Huaian 223003 Peoples R China

The chemical production process typically possesses complexity and high risks. Effective fault diagnosis is a key technology for ensuring the reliability and safety of chemical production processes. In this study, a comprehensive fault diagnosis method based on time-varying filtering empirical mode decomposition (TVF-EMD), kernel principal component analysis (KPCA), and an improved whale optimization algorithm (WOA) to optimize bi-directional long short-term memory (BiLSTM) is proposed. This research utilizes TVF-EMD and KPCA to analyze and preprocess the raw data, eliminating noise and and reducing the dimensions of the fault data. Subsequently, BiLSTM is employed for fault data classification. To address the hyperparameters within BiLSTM, the enhanced WOA is used for optimization. Finally, the efficacy and superiority of this approach are validated through two fault diagnosis examples.

关键词： Fault diagnosis Kernel principal component analysis whale optimization algorithm Bi-directional long short-term memory Tennessee eastman process

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Tribological Parameters optimization of AZ31-SiC Composite Using whale optimization algorithm

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JOURNAL OF MATERIALS ENGINEERING AND PERFORMANCE 2023年第6期32卷 2735-2748页

作者： Kumar, Kothuri Chenchu Kishor Kumar, Bandalamudi Raghu Rao, Nalluri Mohan Gudlavalleru Engn Coll Gudlavalleru 521356 Andhra Pradesh India Pradad V Potluri Siddhartha Inst Technol Vijayawada Andhra Pradesh India Jawaharlal Nehru Technol Univ Kakinada Kakinada Andhra Pradesh India

In this paper, a whale optimization algorithm (WOA) is proposed for the optimization of tribological parameters of AZ31-SiC metal matrix composite. Experiments were carried out by the surface of pin with the different loads of 10, 20, 30 N, velocities of 0.5, 1.0, 1.5 m s(-1) and sliding distances of 500 m, 750 m, 1000 m respectively for optimization. The equations derived from the regression analysis are considered as objective functions to find the optimal parameters of wear and coefficients of friction (COF) using WOA. Considering these objective functions, the WOA is used to optimize the tribological parameters. The optimized parameters obtained from the WOA are compared with the parameters derived from PSO, DE, FFA and experimental results. The optimum wear values obtained from WOA, PSO, DE, FFA and experiment results are 3.55, 3.63, 3.60, 3.57 and 3.77 mg respectively. The optimum values of COF obtained from WOA, PSO, DE, FFA and experiment results are 0.311, 0.313, 0.314, 0.312, and 0.33 respectively. The optimum wear values obtained from WOA, PSO, DE and FFA are 6.01, 3.89, 4.69, 5.48% lower than the experimental value. The optimum COF values obtained from WOA, PSO, DE and FFA are 5.76, 5.15, 4.85, 5.45% lower than the experimental value. It is evidence from the results that WOA has provided the best wear and COF values for AZ31-SiC metal matrix composite when compared to other methods. This proposed method reduces the time and effort of the manufacturer to use the composite material in an application with the optimum operating condition for more life. The microstructural SEM micrographs reveal the distribution of reinforcement in the composite. The SEM micrographs of worn out surfaces present various wear mechanisms of the composites under variety of operating conditions.

关键词： AZ31 alloy regression analysis SiC reinforcement SEM whale optimization algorithm wear properties

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Parallel Enhanced whale optimization algorithm for Independent Tasks Scheduling on Cloud Computing

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IEEE ACCESS 2024年 12卷 23529-23548页

作者： Khan, Zulfiqar Ali Aziz, Izzatdin Abdul Osman, Nurul Aida Bt Nabi, Said Univ Teknol PETRONAS Dept Comp & Informat Sci Seri Iskandar 32610 Malaysia Virtual Univ Pakistan Dept Comp Sci & Informat Technol Lahore 44000 Pakistan

Cloud computing has been imperative for computing systems worldwide since its inception. The researchers strive to leverage the efficient utilization of cloud resources to execute workload quickly in addition to providing better quality of service. Among several challenges on the cloud, task scheduling is one of the fundamental NP-hard problems. Meta-heuristic algorithms are extensively employed to solve task scheduling as a discrete optimization problem and therefore several meta-heuristic algorithms have been developed. However, they have their own strengths and weaknesses. Local optima, poor convergence, high execution time, and scalability are the predominant issues among meta-heuristic algorithms. In this paper, a parallel enhanced whale optimization algorithm is proposed to schedule independent tasks in the cloud with heterogeneous resources. The proposed algorithm improves solution diversity and avoids local optima using a modified encircling maneuver and an adaptive bubble net attacking mechanism. The parallelization technique keeps the execution time low despite its internal complexity. The proposed algorithm minimizes the makespan while improving resource utilization and throughput. It demonstrates the effectiveness of the proposed PEWOA against the best performing enhanced whale optimization algorithm (WOAmM) and Multi-core Random Matrix Particle Swarm optimization (MRMPSO). The algorithm consistently produces better results with varying number of tasks on GoCJ dataset, indicating better scalability. The experiments are conducted in CloudSim utilizing a variety of GoCJ and HCSP instances. Various statistical tests are also conducted to evaluate the significance of the results.

关键词： Task scheduling meta-heuristic whale optimization algorithm cloud computing

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Hybrid enhanced whale optimization algorithm for contrast and detail enhancement of color images

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第1期27卷 231-267页

作者： Braik, Malik Al Balqa Appl Univ Dept Comp Sci Al Salt Jordan

Image enhancement is an essential step in image analysis and processing as it helps people to recognize and understand images because their perception is greatly influenced by image quality. Incomplete beta function (IBF) is a broadly employed transformation function for image contrast enhancement (ICE). However, IBF has low parameter selection efficiency, a limited range of mutable parameters to stretch areas with high or low gray levels, and image enhancement with stretching at both ends is almost ineffectual. In this paper, a hybrid whale optimization algorithm (WOA) with the Chameleon Swarm algorithm (CSA), referred to as HWOA, is presented to adaptively determine the optimal parameters of IBF for ICE. Then, bilateral gamma correction (BGC) is utilized to produce better contrast and brightness while preserving edge detail. The proposed HWOA algorithm follows a multi-phased process of strategies. Many improvements were made to the mathematical model of WOA followed by its hybridization with CSA for further exploration and exploitation aspects. The proposed HWOA is tested over some standard images along with well-known available Kodak image dataset and assessed using several standard measures. The experimental results showed that the proposed algorithm can satisfactorily surpass many other algorithms that used the same image enhancement approach as well as other traditional image enhancement methods deemed here for comparison. Specifically, the findings on ten color images revealed that the performance of HWOA in terms of average peak signal-to-noise ratio, average structural similarity index, and average values of entropy results are more than 33.0, 96.6%, and 7.3, respectively, and these results are much better than all other comparative methods in the corresponding evaluation criteria.

关键词： Image enhancement whale optimization algorithm Incomplete beta function optimization Bilateral gamma correction Meta-heuristic

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Multi-spectral evaluation of total nitrogen, phosphorus and potassium content in soil using Vis-NIR spectroscopy based on a modified support vector machine with whale optimization algorithm

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SOIL & TILLAGE RESEARCH 2025年 252卷

作者： Liu, Mochen Yang, Kuankuan Yan, Yinfa Song, Zhanhua Tian, Fuyang Li, Fade Yu, Zhenwei Zhang, Rongyao Yang, Qinglu Lu, Yao Shandong Agr Univ Coll Mech & Elect Engn Tai An 271018 Peoples R China Shandong Key Lab Hort Machineries & Equipment Tai An 271018 Peoples R China Shandong Engn Lab Agr Equipment Intelligence Tai An 271018 Peoples R China Weichai Lovol Intelligent Agr Technol Co Ltd Weifang 261206 Peoples R China

Accurate and non-destructive detection of total nitrogen (TN), total phosphorus (TP), and total potassium (TK) levels in soil is crucial for precise soil testing and fertilization in modernized precision agriculture. Traditional methods for soil composition analysis are expensive, time-consuming, and destructive. This research aims to establish a low-cost, high-precision, and non-destructive method for soil nutrient detection based on visible-near-infrared (Vis-NIR) spectroscopy (350-2500 nm) combined with improved machine learning algorithms. The VisNIR spectra of soil samples were acquired using the RS-5400 high-resolution ground feature spectrometer. Subsequently, the Monte Carlo sampling cross-validation (MCCV) algorithm was used to eliminate abnormal samples, and then different preprocessing methods were performed on the spectral data including first-derivative (FD), Savitzky-Golay smoothing (SG) and others. The optimal preprocessing method was selected from these options. In order to remove redundant information and increase the speed of calculation, five algorithms such as competitive adaptive reweighted sampling (CARS), iteratively retains informative variables (IRIV) and the variable iterative space shrinkage approach (VISSA)-IRIV algorithm were used to select feature variables. The characteristic wavelengths closely related to TN, TP, and TK in the soil have been extracted. Then, the RBF kernel (radial basis function) and poly kernel were mixed to obtain the RBF-poly hybrid kernel function, and then the hybrid kernel function support vector machine (RBF-poly-SVM) and the radial basis kernel function support vector machine (RBF-SVM) were applied respectively. Establish prediction models and introduce the whale optimization algorithm (WOA) to optimize the g (kernel function parameter), c (penalty factor) and k-rbf (weight coefficient) parameters in the two models. The performance of the developed models was tested using the coefficient of determination (R2),

关键词： Soil Vis-NIR Nitrogen whale optimization algorithm Support vector machine

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