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  • Open Access

    ARTICLE

    Advanced Machine Learning and Gene Expression Programming Techniques for Predicting CO2-Induced Alterations in Coal Strength

    Zijian Liu1, Yong Shi2, Chuanqi Li1, Xiliang Zhang3,*, Jian Zhou1, Manoj Khandelwal4,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 153-183, 2025, DOI:10.32604/cmes.2025.062426 - 11 April 2025

    Abstract Given the growing concern over global warming and the critical role of carbon dioxide (CO2) in this phenomenon, the study of CO2-induced alterations in coal strength has garnered significant attention due to its implications for carbon sequestration. A large number of experiments have proved that CO2 interaction time (T), saturation pressure (P) and other parameters have significant effects on coal strength. However, accurate evaluation of CO2-induced alterations in coal strength is still a difficult problem, so it is particularly important to establish accurate and efficient prediction models. This study explored the application of advanced machine learning (ML)… More >

  • Open Access

    ARTICLE

    Multi-Objective Approaches for Optimizing 37-Bus Power Distribution Systems with Reconfiguration Technique: From Unbalance Current & Voltage Factor to Reliability Indices

    Murat Cikan*

    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 673-721, 2025, DOI:10.32604/cmes.2025.061699 - 11 April 2025

    Abstract This study examines various issues arising in three-phase unbalanced power distribution networks (PDNs) using a comprehensive optimization approach. With the integration of renewable energy sources, increasing energy demands, and the adoption of smart grid technologies, power systems are undergoing a rapid transformation, making the need for efficient, reliable, and sustainable distribution networks increasingly critical. In this paper, the reconfiguration problem in a 37-bus unbalanced PDN test system is solved using five different popular metaheuristic algorithms. Among these advanced search algorithms, the Bonobo Optimizer (BO) has demonstrated superior performance in handling the complexities of unbalanced power… More >

  • Open Access

    ARTICLE

    Phasmatodea Population Evolution Algorithm Based on Spiral Mechanism and Its Application to Data Clustering

    Jeng-Shyang Pan1,2,3, Mengfei Zhang1, Shu-Chuan Chu2,*, Xingsi Xue4, Václav Snášel5

    CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 475-496, 2025, DOI:10.32604/cmc.2025.060170 - 26 March 2025

    Abstract Data clustering is an essential technique for analyzing complex datasets and continues to be a central research topic in data analysis. Traditional clustering algorithms, such as K-means, are widely used due to their simplicity and efficiency. This paper proposes a novel Spiral Mechanism-Optimized Phasmatodea Population Evolution Algorithm (SPPE) to improve clustering performance. The SPPE algorithm introduces several enhancements to the standard Phasmatodea Population Evolution (PPE) algorithm. Firstly, a Variable Neighborhood Search (VNS) factor is incorporated to strengthen the local search capability and foster population diversity. Secondly, a position update model, incorporating a spiral mechanism, is… More >

  • Open Access

    ARTICLE

    Improved Cyclic System Based Optimization Algorithm (ICSBO)

    Yanjiao Wang, Zewei Nan*

    CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4709-4740, 2025, DOI:10.32604/cmc.2025.058894 - 06 March 2025

    Abstract Cyclic-system-based optimization (CSBO) is an innovative metaheuristic algorithm (MHA) that draws inspiration from the workings of the human blood circulatory system. However, CSBO still faces challenges in solving complex optimization problems, including limited convergence speed and a propensity to get trapped in local optima. To improve the performance of CSBO further, this paper proposes improved cyclic-system-based optimization (ICSBO). First, in venous blood circulation, an adaptive parameter that changes with evolution is introduced to improve the balance between convergence and diversity in this stage and enhance the exploration of search space. Second, the simplex method strategy… More >

  • Open Access

    REVIEW

    Stochastic Fractal Search: A Decade Comprehensive Review on Its Theory, Variants, and Applications

    Mohammed A. El-Shorbagy1, Anas Bouaouda2,*, Laith Abualigah3,4, Fatma A. Hashim5,6

    CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2339-2404, 2025, DOI:10.32604/cmes.2025.061028 - 03 March 2025

    Abstract With the rapid advancements in technology and science, optimization theory and algorithms have become increasingly important. A wide range of real-world problems is classified as optimization challenges, and meta-heuristic algorithms have shown remarkable effectiveness in solving these challenges across diverse domains, such as machine learning, process control, and engineering design, showcasing their capability to address complex optimization problems. The Stochastic Fractal Search (SFS) algorithm is one of the most popular meta-heuristic optimization methods inspired by the fractal growth patterns of natural materials. Since its introduction by Hamid Salimi in 2015, SFS has garnered significant attention… More >

  • Open Access

    ARTICLE

    Heuristic-Based Optimal Load Frequency Control with Offsite Backup Controllers in Interconnected Microgrids

    Aijia Ding, Tingzhang Liu*

    Energy Engineering, Vol.121, No.12, pp. 3735-3759, 2024, DOI:10.32604/ee.2024.054687 - 22 November 2024

    Abstract The primary factor contributing to frequency instability in microgrids is the inherent intermittency of renewable energy sources. This paper introduces novel dual-backup controllers utilizing advanced fractional order proportional integral derivative (FOPID) controllers to enhance frequency and tie-line power stability in microgrids amid increasing renewable energy integration. To improve load frequency control, the proposed controllers are applied to a two-area interconnected microgrid system incorporating diverse energy sources, such as wind turbines, photovoltaic cells, diesel generators, and various storage technologies. A novel meta-heuristic algorithm is adopted to select the optimal parameters of the proposed controllers. The efficacy… More >

  • Open Access

    REVIEW

    A Critical Review of Active Distribution Network Reconfiguration: Concepts, Development, and Perspectives

    Bo Yang1, Rui Zhang1, Jie Zhang2, Xianlong Cheng2, Jiale Li3, Yimin Zhou1, Yuanweiji Hu1, Bin He1, Gongshuai Zhang4, Xiuping Du4, Si Ji5, Yiyan Sang6, Zhengxun Guo7,8,*

    Energy Engineering, Vol.121, No.12, pp. 3487-3547, 2024, DOI:10.32604/ee.2024.054662 - 22 November 2024

    Abstract In recent years, the large-scale grid connection of various distributed power sources has made the planning and operation of distribution grids increasingly complex. Consequently, a large number of active distribution network reconfiguration techniques have emerged to reduce system losses, improve system safety, and enhance power quality via switching switches to change the system topology while ensuring the radial structure of the network. While scholars have previously reviewed these methods, they all have obvious shortcomings, such as a lack of systematic integration of methods, vague classification, lack of constructive suggestions for future study, etc. Therefore, this… More >

  • Open Access

    ARTICLE

    Q-Learning-Assisted Meta-Heuristics for Scheduling Distributed Hybrid Flow Shop Problems

    Qianyao Zhu1, Kaizhou Gao1,*, Wuze Huang1, Zhenfang Ma1, Adam Slowik2

    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3573-3589, 2024, DOI:10.32604/cmc.2024.055244 - 12 September 2024

    Abstract The flow shop scheduling problem is important for the manufacturing industry. Effective flow shop scheduling can bring great benefits to the industry. However, there are few types of research on Distributed Hybrid Flow Shop Problems (DHFSP) by learning assisted meta-heuristics. This work addresses a DHFSP with minimizing the maximum completion time (Makespan). First, a mathematical model is developed for the concerned DHFSP. Second, four Q-learning-assisted meta-heuristics, e.g., genetic algorithm (GA), artificial bee colony algorithm (ABC), particle swarm optimization (PSO), and differential evolution (DE), are proposed. According to the nature of DHFSP, six local search operations… More >

  • Open Access

    ARTICLE

    An Improved Lung Cancer Segmentation Based on Nature-Inspired Optimization Approaches

    Shazia Shamas1, Surya Narayan Panda1,*, Ishu Sharma1,*, Kalpna Guleria1, Aman Singh2,3,4, Ahmad Ali AlZubi5, Mallak Ahmad AlZubi6

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1051-1075, 2024, DOI:10.32604/cmes.2023.030712 - 17 November 2023

    Abstract The distinction and precise identification of tumor nodules are crucial for timely lung cancer diagnosis and planning intervention. This research work addresses the major issues pertaining to the field of medical image processing while focusing on lung cancer Computed Tomography (CT) images. In this context, the paper proposes an improved lung cancer segmentation technique based on the strengths of nature-inspired approaches. The better resolution of CT is exploited to distinguish healthy subjects from those who have lung cancer. In this process, the visual challenges of the K-means are addressed with the integration of four nature-inspired… More >

  • Open Access

    ARTICLE

    A Productivity Prediction Method Based on Artificial Neural Networks and Particle Swarm Optimization for Shale-Gas Horizontal Wells

    Bin Li*

    FDMP-Fluid Dynamics & Materials Processing, Vol.19, No.10, pp. 2729-2748, 2023, DOI:10.32604/fdmp.2023.029649 - 25 June 2023

    Abstract In order to overcome the deficiencies of current methods for the prediction of the productivity of shale gas horizontal wells after fracturing, a new sophisticated approach is proposed in this study. This new model stems from the combination several techniques, namely, artificial neural network (ANN), particle swarm optimization (PSO), Imperialist Competitive Algorithms (ICA), and Ant Clony Optimization (ACO). These are properly implemented by using the geological and engineering parameters collected from 317 wells. The results show that the optimum PSO-ANN model has a high accuracy, obtaining a R2 of 0.847 on the testing. The partial dependence More >

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