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  • Open Access

    ARTICLE

    Multi-Strategy Assisted Multi-Objective Whale Optimization Algorithm for Feature Selection

    Deng Yang1, Chong Zhou1,*, Xuemeng Wei2, Zhikun Chen3, Zheng Zhang4

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1563-1593, 2024, DOI:10.32604/cmes.2024.048049

    Abstract In classification problems, datasets often contain a large amount of features, but not all of them are relevant for accurate classification. In fact, irrelevant features may even hinder classification accuracy. Feature selection aims to alleviate this issue by minimizing the number of features in the subset while simultaneously minimizing the classification error rate. Single-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 parameter. To eliminate this parameter’s influence, the problem can be reformulated as a multi-objective optimization problem. The… More >

  • Open Access

    ARTICLE

    MCWOA Scheduler: Modified Chimp-Whale Optimization Algorithm for Task Scheduling in Cloud Computing

    Chirag Chandrashekar1, Pradeep Krishnadoss1,*, Vijayakumar Kedalu Poornachary1, Balasundaram Ananthakrishnan1,2

    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2593-2616, 2024, DOI:10.32604/cmc.2024.046304

    Abstract Cloud computing provides a diverse and adaptable resource pool over the internet, allowing users to tap into various resources as needed. It has been seen as a robust solution to relevant challenges. A significant delay can hamper the performance of IoT-enabled cloud platforms. However, efficient task scheduling can lower the cloud infrastructure’s energy consumption, thus maximizing the service provider’s revenue by decreasing user job processing times. The proposed Modified Chimp-Whale Optimization Algorithm called Modified Chimp-Whale Optimization Algorithm (MCWOA), combines elements of the Chimp Optimization Algorithm (COA) and the Whale Optimization Algorithm (WOA). To enhance MCWOA’s… More >

  • Open Access

    ARTICLE

    An Improved Whale Optimization Algorithm for Global Optimization and Realized Volatility Prediction

    Xiang Wang1, Liangsa Wang2,*, Han Li1, Yibin Guo1

    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 2935-2969, 2023, DOI:10.32604/cmc.2023.044948

    Abstract The original whale optimization algorithm (WOA) has a low initial population quality and tends to converge to local optimal solutions. To address these challenges, this paper introduces an improved whale optimization algorithm called OLCHWOA, incorporating a chaos mechanism and an opposition-based learning strategy. This algorithm introduces chaotic initialization and opposition-based initialization operators during the population initialization phase, thereby enhancing the quality of the initial whale population. Additionally, including an elite opposition-based learning operator significantly improves the algorithm’s global search capabilities during iterations. The work and contributions of this paper are primarily reflected in two aspects.… More >

  • Open Access

    ARTICLE

    An Optimal Node Localization in WSN Based on Siege Whale Optimization Algorithm

    Thi-Kien Dao1, Trong-The Nguyen1,2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2201-2237, 2024, DOI:10.32604/cmes.2023.029880

    Abstract Localization or positioning scheme in Wireless sensor networks (WSNs) is one of the most challenging and fundamental operations in various monitoring or tracking applications because the network deploys a large area and allocates the acquired location information to unknown devices. The metaheuristic approach is one of the most advantageous ways to deal with this challenging issue and overcome the disadvantages of the traditional methods that often suffer from computational time problems and small network deployment scale. This study proposes an enhanced whale optimization algorithm that is an advanced metaheuristic algorithm based on the siege mechanism… More >

  • Open Access

    ARTICLE

    Automated Pavement Crack Detection Using Deep Feature Selection and Whale Optimization Algorithm

    Shorouq Alshawabkeh, Li Wu*, Daojun Dong, Yao Cheng, Liping Li, Mohammad Alanaqreh

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 63-77, 2023, DOI:10.32604/cmc.2023.042183

    Abstract Pavement crack detection plays a crucial role in ensuring road safety and reducing maintenance expenses. Recent advancements in deep learning (DL) techniques have shown promising results in detecting pavement cracks; however, the selection of relevant features for classification remains challenging. In this study, we propose a new approach for pavement crack detection that integrates deep learning for feature extraction, the whale optimization algorithm (WOA) for feature selection, and random forest (RF) for classification. The performance of the models was evaluated using accuracy, recall, precision, F1 score, and area under the receiver operating characteristic curve (AUC).… More >

  • Open Access

    ARTICLE

    Advanced Guided Whale Optimization Algorithm for Feature Selection in BlazePose Action Recognition

    Motasem S. Alsawadi1,*, El-Sayed M. El-kenawy2, Miguel Rio1

    Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2767-2782, 2023, DOI:10.32604/iasc.2023.039440

    Abstract The BlazePose, which models human body skeletons as spatiotemporal graphs, has achieved fantastic performance in skeleton-based action identification. Skeleton extraction from photos for mobile devices has been made possible by the BlazePose system. A Spatial-Temporal Graph Convolutional Network (STGCN) can then forecast the actions. The Spatial-Temporal Graph Convolutional Network (STGCN) can be improved by simply replacing the skeleton input data with a different set of joints that provide more information about the activity of interest. On the other hand, existing approaches require the user to manually set the graph’s topology and then fix it across… More >

  • Open Access

    ARTICLE

    Predicting Reliability and Remaining Useful Life of Rolling Bearings Based on Optimized Neural Networks

    Tiantian Liang*, Runze Wang, Xuxiu Zhang, Yingdong Wang, Jianxiong Yang

    Structural Durability & Health Monitoring, Vol.17, No.5, pp. 433-455, 2023, DOI:10.32604/sdhm.2023.029331

    Abstract In this study, an optimized long short-term memory (LSTM) network is proposed to predict the reliability and remaining useful life (RUL) of rolling bearings based on an improved whale-optimized algorithm (IWOA). The multi-domain features are extracted to construct the feature dataset because the single-domain features are difficult to characterize the performance degeneration of the rolling bearing. To provide covariates for reliability assessment, a kernel principal component analysis is used to reduce the dimensionality of the features. A Weibull distribution proportional hazard model (WPHM) is used for the reliability assessment of rolling bearing, and a beluga… More > Graphic Abstract

    Predicting Reliability and Remaining Useful Life of Rolling Bearings Based on Optimized Neural Networks

  • Open Access

    ARTICLE

    Safety Assessment of Liquid Launch Vehicle Structures Based on Interpretable Belief Rule Base

    Gang Xiang1,2, Xiaoyu Cheng3, Wei He3,4,*, Peng Han3

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 273-298, 2023, DOI:10.32604/csse.2023.037892

    Abstract A liquid launch vehicle is an important carrier in aviation, and its regular operation is essential to maintain space security. In the safety assessment of fluid launch vehicle body structure, it is necessary to ensure that the assessment model can learn self-response rules from various uncertain data and not differently to provide a traceable and interpretable assessment process. Therefore, a belief rule base with interpretability (BRB-i) assessment method of liquid launch vehicle structure safety status combines data and knowledge. Moreover, an innovative whale optimization algorithm with interpretable constraints is proposed. The experiments are carried out… More >

  • Open Access

    ARTICLE

    A Whale Optimization Algorithm with Distributed Collaboration and Reverse Learning Ability

    Zhedong Xu*, Yongbo Su, Fang Yang, Ming Zhang

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5965-5986, 2023, DOI:10.32604/cmc.2023.037611

    Abstract Due to the development of digital transformation, intelligent algorithms are getting more and more attention. The whale optimization algorithm (WOA) is one of swarm intelligence optimization algorithms and is widely used to solve practical engineering optimization problems. However, with the increased dimensions, higher requirements are put forward for algorithm performance. The double population whale optimization algorithm with distributed collaboration and reverse learning ability (DCRWOA) is proposed to solve the slow convergence speed and unstable search accuracy of the WOA algorithm in optimization problems. In the DCRWOA algorithm, the novel double population search strategy is constructed. More >

  • Open Access

    ARTICLE

    PREDICTION MODEL OF WAX DEPOSITION RATE BASED ON WOABPNN ALGORITHM

    Rongge Xiaoa,* , Qi Zhuanga, Shuaishuai Jina , Wenbo Jina

    Frontiers in Heat and Mass Transfer, Vol.18, pp. 1-7, 2022, DOI:10.5098/hmt.18.8

    Abstract A model for predicting wax deposition rate in pipeline transportation is constructed to predict wax deposition in actual pipeline, which can provide decision support for the flow guarantee of waxy crude oil in pipeline transportation. This paper analyzes the working principle of Back Propagation Neural Networks (BPNN). Aiming at the problems of BPNN model, such as over learning, long training time, low generalization ability and easy to fall into local minimum, the paper proposes an improved scheme of using Whale Optimization Algorithm (WOA) to optimize BPNN model(WOABPNN).Taking 38 groups of crude oil wax deposition experimental More >

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