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  • Open Access


    Advanced Machine Learning Methods for Prediction of Blast-Induced Flyrock Using Hybrid SVR Methods

    Ji Zhou1,2, Yijun Lu3, Qiong Tian1,2, Haichuan Liu3, Mahdi Hasanipanah4,5,*, Jiandong Huang3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1595-1617, 2024, DOI:10.32604/cmes.2024.048398

    Abstract Blasting in surface mines aims to fragment rock masses to a proper size. However, flyrock is an undesirable effect of blasting that can result in human injuries. In this study, support vector regression (SVR) is combined with four algorithms: gravitational search algorithm (GSA), biogeography-based optimization (BBO), ant colony optimization (ACO), and whale optimization algorithm (WOA) for predicting flyrock in two surface mines in Iran. Additionally, three other methods, including artificial neural network (ANN), kernel extreme learning machine (KELM), and general regression neural network (GRNN), are employed, and their performances are compared to those of four hybrid SVR models. After modeling,… More >

  • Open Access


    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 Whale Optimization Algorithm (WOA) is… More >

  • Open Access


    Adaptive Network Sustainability and Defense Based on Artificial Bees Colony Optimization Algorithm for Nature Inspired Cyber Security

    Chirag Ganguli1, Shishir Kumar Shandilya2, Michal Gregus3, Oleh Basystiuk4,*

    Computer Systems Science and Engineering, Vol.48, No.3, pp. 739-758, 2024, DOI:10.32604/csse.2024.042607

    Abstract Cyber Defense is becoming a major issue for every organization to keep business continuity intact. The presented paper explores the effectiveness of a meta-heuristic optimization algorithm-Artificial Bees Colony Algorithm (ABC) as an Nature Inspired Cyber Security mechanism to achieve adaptive defense. It experiments on the Denial-Of-Service attack scenarios which involves limiting the traffic flow for each node. Businesses today have adapted their service distribution models to include the use of the Internet, allowing them to effectively manage and interact with their customer data. This shift has created an increased reliance on online services to store vast amounts of confidential customer… More >

  • Open Access


    Ensemble Deep Learning Based Air Pollution Prediction for Sustainable Smart Cities

    Maha Farouk Sabir1, Mahmoud Ragab2,3,*, Adil O. Khadidos2, Khaled H. Alyoubi1, Alaa O. Khadidos1,4

    Computer Systems Science and Engineering, Vol.48, No.3, pp. 627-643, 2024, DOI:10.32604/csse.2023.041551

    Abstract Big data and information and communication technologies can be important to the effectiveness of smart cities. Based on the maximal attention on smart city sustainability, developing data-driven smart cities is newly obtained attention as a vital technology for addressing sustainability problems. Real-time monitoring of pollution allows local authorities to analyze the present traffic condition of cities and make decisions. Relating to air pollution occurs a main environmental problem in smart city environments. The effect of the deep learning (DL) approach quickly increased and penetrated almost every domain, comprising air pollution forecast. Therefore, this article develops a new Coot Optimization Algorithm… More >

  • Open Access


    A Reference Vector-Assisted Many-Objective Optimization Algorithm with Adaptive Niche Dominance Relation

    Fangzhen Ge1,3, Yating Wu1,*, Debao Chen2,4, Longfeng Shen1,5

    Intelligent Automation & Soft Computing, Vol.39, No.2, pp. 189-211, 2024, DOI:10.32604/iasc.2024.042841

    Abstract It is still a huge challenge for traditional Pareto-dominated many-objective optimization algorithms to solve many-objective optimization problems because these algorithms hardly maintain the balance between convergence and diversity and can only find a group of solutions focused on a small area on the Pareto front, resulting in poor performance of those algorithms. For this reason, we propose a reference vector-assisted algorithm with an adaptive niche dominance relation, for short MaOEA-AR. The new dominance relation forms a niche based on the angle between candidate solutions. By comparing these solutions, the solution with the best convergence is found to be the non-dominated… More >

  • Open Access


    Improved Particle Swarm Optimization for Parameter Identification of Permanent Magnet Synchronous Motor

    Shuai Zhou1, Dazhi Wang1,*, Yongliang Ni2, Keling Song2, Yanming Li2

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2187-2207, 2024, DOI:10.32604/cmc.2024.048859

    Abstract In the process of identifying parameters for a permanent magnet synchronous motor, the particle swarm optimization method is prone to being stuck in local optima in the later stages of iteration, resulting in low parameter accuracy. This work proposes a fuzzy particle swarm optimization approach based on the transformation function and the filled function. This approach addresses the topic of particle swarm optimization in parameter identification from two perspectives. Firstly, the algorithm uses a transformation function to change the form of the fitness function without changing the position of the extreme point of the fitness function, making the extreme point… More >

  • Open Access


    Multi-Objective Optimization Algorithm for Grouping Decision Variables Based on Extreme Point Pareto Frontier

    Jun Wang1,2, Linxi Zhang1,2, Hao Zhang1, Funan Peng1,*, Mohammed A. El-Meligy3, Mohamed Sharaf3, Qiang Fu1

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1281-1299, 2024, DOI:10.32604/cmc.2024.048495

    Abstract The existing algorithms for solving multi-objective optimization problems fall into three main categories: Decomposition-based, dominance-based, and indicator-based. Traditional multi-objective optimization problems mainly focus on objectives, treating decision variables as a total variable to solve the problem without considering the critical role of decision variables in objective optimization. As seen, a variety of decision variable grouping algorithms have been proposed. However, these algorithms are relatively broad for the changes of most decision variables in the evolution process and are time-consuming in the process of finding the Pareto frontier. To solve these problems, a multi-objective optimization algorithm for grouping decision variables based… More >

  • Open Access


    A Spectral Convolutional Neural Network Model Based on Adaptive Fick’s Law for Hyperspectral Image Classification

    Tsu-Yang Wu1,2, Haonan Li2, Saru Kumari3, Chien-Ming Chen1,*

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 19-46, 2024, DOI:10.32604/cmc.2024.048347

    Abstract Hyperspectral image classification stands as a pivotal task within the field of remote sensing, yet achieving high-precision classification remains a significant challenge. In response to this challenge, a Spectral Convolutional Neural Network model based on Adaptive Fick’s Law Algorithm (AFLA-SCNN) is proposed. The Adaptive Fick’s Law Algorithm (AFLA) constitutes a novel metaheuristic algorithm introduced herein, encompassing three new strategies: Adaptive weight factor, Gaussian mutation, and probability update policy. With adaptive weight factor, the algorithm can adjust the weights according to the change in the number of iterations to improve the performance of the algorithm. Gaussian mutation helps the algorithm avoid… More >

  • Open Access


    Large-Scale Multi-Objective Optimization Algorithm Based on Weighted Overlapping Grouping of Decision Variables

    Liang Chen1, Jingbo Zhang1, Linjie Wu1, Xingjuan Cai1,2,*, Yubin Xu1

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 363-383, 2024, DOI:10.32604/cmes.2024.049044

    Abstract The large-scale multi-objective optimization algorithm (LSMOA), based on the grouping of decision variables, is an advanced method for handling high-dimensional decision variables. However, in practical problems, the interaction among decision variables is intricate, leading to large group sizes and suboptimal optimization effects; hence a large-scale multi-objective optimization algorithm based on weighted overlapping grouping of decision variables (MOEAWOD) is proposed in this paper. Initially, the decision variables are perturbed and categorized into convergence and diversity variables; subsequently, the convergence variables are subdivided into groups based on the interactions among different decision variables. If the size of a group surpasses the set… More >

  • Open Access


    Predicting Rock Burst in Underground Engineering Leveraging a Novel Metaheuristic-Based LightGBM Model

    Kai Wang1, Biao He2,*, Pijush Samui3, Jian Zhou4

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 229-253, 2024, DOI:10.32604/cmes.2024.047569

    Abstract Rock bursts represent a formidable challenge in underground engineering, posing substantial risks to both infrastructure and human safety. These sudden and violent failures of rock masses are characterized by the rapid release of accumulated stress within the rock, leading to severe seismic events and structural damage. Therefore, the development of reliable prediction models for rock bursts is paramount to mitigating these hazards. This study aims to propose a tree-based model—a Light Gradient Boosting Machine (LightGBM)—to predict the intensity of rock bursts in underground engineering. 322 actual rock burst cases are collected to constitute an exhaustive rock burst dataset, which serves… More >

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