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  • Open Access

    ARTICLE

    Hybrid Deep Learning and Optimized Feature Selection for Oil Spill Detection in Satellite Images

    Ghada Atteia1,*, Mohammed Dabboor2, Konstantinos Karantzalos3, Maali Alabdulhafith1

    CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1747-1767, 2025, DOI:10.32604/cmc.2025.063363 - 09 June 2025

    Abstract This study explores the integration of Synthetic Aperture Radar (SAR) imagery with deep learning and metaheuristic feature optimization techniques for enhanced oil spill detection. This study proposes a novel hybrid approach for oil spill detection. The introduced approach integrates deep transfer learning with the metaheuristic Binary Harris Hawk optimization (BHHO) and Principal Component Analysis (PCA) for improved feature extraction and selection from input SAR imagery. Feature transfer learning of the MobileNet convolutional neural network was employed to extract deep features from the SAR images. The BHHO and PCA algorithms were implemented to identify subsets of… More >

  • Open Access

    ARTICLE

    Metaheuristic-Driven Abnormal Traffic Detection Model for SDN Based on Improved Tyrannosaurus Optimization Algorithm

    Hui Xu, Jiahui Chen*, Zhonghao Hu

    CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4495-4513, 2025, DOI:10.32604/cmc.2025.062189 - 19 May 2025

    Abstract Nowadays, abnormal traffic detection for Software-Defined Networking (SDN) faces the challenges of large data volume and high dimensionality. Since traditional machine learning-based detection methods have the problem of data redundancy, the Metaheuristic Algorithm (MA) is introduced to select features before machine learning to reduce the dimensionality of data. Since a Tyrannosaurus Optimization Algorithm (TROA) has the advantages of few parameters, simple implementation, and fast convergence, and it shows better results in feature selection, TROA can be applied to abnormal traffic detection for SDN. However, TROA suffers from insufficient global search capability, is easily trapped in… More >

  • Open Access

    ARTICLE

    Barber Optimization Algorithm: A New Human-Based Approach for Solving Optimization Problems

    Tareq Hamadneh1, Belal Batiha2, Omar Alsayyed3, Widi Aribowo4, Zeinab Montazeri5, Mohammad Dehghani5,*, Frank Werner6,*, Haider Ali7, Riyadh Kareem Jawad8, Ibraheem Kasim Ibraheem9, Kei Eguchi10

    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2677-2718, 2025, DOI:10.32604/cmc.2025.064087 - 16 April 2025

    Abstract In this study, a completely different approach to optimization is introduced through the development of a novel metaheuristic algorithm called the Barber Optimization Algorithm (BaOA). Inspired by the human interactions between barbers and customers, BaOA captures two key processes: the customer’s selection of a hairstyle and the detailed refinement during the haircut. These processes are translated into a mathematical framework that forms the foundation of BaOA, consisting of two critical phases: exploration, representing the creative selection process, and exploitation, which focuses on refining details for optimization. The performance of BaOA is evaluated using 52 standard… More >

  • Open Access

    ARTICLE

    SL-COA: Hybrid Efficient and Enhanced Coati Optimization Algorithm for Structural Reliability Analysis

    Yunhan Ling1, Huajun Peng2, Yiqing Shi1,*, Chao Xu1, Jingzhen Yan1, Jingjing Wang1, Hui Ma3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 767-808, 2025, DOI:10.32604/cmes.2025.061763 - 11 April 2025

    Abstract The traditional first-order reliability method (FORM) often encounters challenges with non-convergence of results or excessive calculation when analyzing complex engineering problems. To improve the global convergence speed of structural reliability analysis, an improved coati optimization algorithm (COA) is proposed in this paper. In this study, the social learning strategy is used to improve the coati optimization algorithm (SL-COA), which improves the convergence speed and robustness of the new heuristic optimization algorithm. Then, the SL-COA is compared with the latest heuristic optimization algorithms such as the original COA, whale optimization algorithm (WOA), and osprey optimization algorithm… More >

  • Open Access

    REVIEW

    Patterns in Heuristic Optimization Algorithms: A Comprehensive Analysis

    Robertas Damasevicius*

    CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 1493-1538, 2025, DOI:10.32604/cmc.2024.057431 - 17 February 2025

    Abstract Heuristic optimization algorithms have been widely used in solving complex optimization problems in various fields such as engineering, economics, and computer science. These algorithms are designed to find high-quality solutions efficiently by balancing exploration of the search space and exploitation of promising solutions. While heuristic optimization algorithms vary in their specific details, they often exhibit common patterns that are essential to their effectiveness. This paper aims to analyze and explore common patterns in heuristic optimization algorithms. Through a comprehensive review of the literature, we identify the patterns that are commonly observed in these algorithms, including… More >

  • Open Access

    ARTICLE

    Artificial Circulation System Algorithm: A Novel Bio-Inspired Algorithm

    Nermin Özcan1,2,*, Semih Utku3, Tolga Berber4

    CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 635-663, 2025, DOI:10.32604/cmes.2024.055860 - 17 December 2024

    Abstract Metaheuristics are commonly used in various fields, including real-life problem-solving and engineering applications. The present work introduces a novel metaheuristic algorithm named the Artificial Circulatory System Algorithm (ACSA). The control of the circulatory system inspires it and mimics the behavior of hormonal and neural regulators involved in this process. The work initially evaluates the effectiveness of the suggested approach on 16 two-dimensional test functions, identified as classical benchmark functions. The method was subsequently examined by application to 12 CEC 2022 benchmark problems of different complexities. Furthermore, the paper evaluates ACSA in comparison to 64 metaheuristic… More >

  • Open Access

    ARTICLE

    Hybrid Metaheuristic Lion and Firefly Optimization Algorithm with Chaotic Map for Substitution S-Box Design

    Arkan Kh Shakr Sabonchi*

    Journal of Information Hiding and Privacy Protection, Vol.6, pp. 21-45, 2024, DOI:10.32604/jihpp.2024.058954 - 31 December 2024

    Abstract Substitution boxes (S-boxes) are key components of symmetrical cryptosystems, acting as nonlinear substitution functions that hide the relationship between the encrypted text and input key. This confusion mechanism is vital for cryptographic security because it prevents attackers from intercepting the secret key by analyzing the encrypted text. Therefore, the S-box design is essential for the robustness of cryptographic systems, especially for the data encryption standard (DES) and advanced encryption standard (AES). This study focuses on the application of the firefly algorithm (FA) and metaheuristic lion optimization algorithm (LOA), thereby proposing a hybrid approach called the… More >

  • Open Access

    ARTICLE

    Using the Novel Wolverine Optimization Algorithm for Solving Engineering Applications

    Tareq Hamadneh1, Belal Batiha2, Omar Alsayyed3, Frank Werner4,*, Zeinab Monrazeri5, Mohammad Dehghani5,*, Kei Eguchi6

    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2253-2323, 2024, DOI:10.32604/cmes.2024.055171 - 31 October 2024

    Abstract This paper introduces the Wolverine Optimization Algorithm (WoOA), a biomimetic method inspired by the foraging behaviors of wolverines in their natural habitats. WoOA innovatively integrates two primary strategies: scavenging and hunting, mirroring the wolverine’s adeptness in locating carrion and pursuing live prey. The algorithm’s uniqueness lies in its faithful simulation of these dual strategies, which are mathematically structured to optimize various types of problems effectively. The effectiveness of WoOA is rigorously evaluated using the Congress on Evolutionary Computation (CEC) 2017 test suite across dimensions of 10, 30, 50, and 100. The results showcase WoOA’s robust… More >

  • Open Access

    ARTICLE

    African Bison Optimization Algorithm: A New Bio-Inspired Optimizer with Engineering Applications

    Jian Zhao1,2,*, Kang Wang1,2, Jiacun Wang3,*, Xiwang Guo4, Liang Qi5

    CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 603-623, 2024, DOI:10.32604/cmc.2024.050523 - 15 October 2024

    Abstract This paper introduces the African Bison Optimization (ABO) algorithm, which is based on biological population. ABO is inspired by the survival behaviors of the African bison, including foraging, bathing, jousting, mating, and eliminating. The foraging behavior prompts the bison to seek a richer food source for survival. When bison find a food source, they stick around for a while by bathing behavior. The jousting behavior makes bison stand out in the population, then the winner gets the chance to produce offspring in the mating behavior. The eliminating behavior causes the old or injured bison to More >

  • Open Access

    ARTICLE

    Ensemble Filter-Wrapper Text Feature Selection Methods for Text Classification

    Oluwaseun Peter Ige1,2, Keng Hoon Gan1,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.2, pp. 1847-1865, 2024, DOI:10.32604/cmes.2024.053373 - 27 September 2024

    Abstract Feature selection is a crucial technique in text classification for improving the efficiency and effectiveness of classifiers or machine learning techniques by reducing the dataset’s dimensionality. This involves eliminating irrelevant, redundant, and noisy features to streamline the classification process. Various methods, from single feature selection techniques to ensemble filter-wrapper methods, have been used in the literature. Metaheuristic algorithms have become popular due to their ability to handle optimization complexity and the continuous influx of text documents. Feature selection is inherently multi-objective, balancing the enhancement of feature relevance, accuracy, and the reduction of redundant features. This… More >

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