Farhad Soleimanian Gharehchopogh^*, Keyvan Fattahi Rishakan

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.073808 - 29 January 2026

Abstract Optimization algorithms are crucial for solving NP-hard problems in engineering and computational sciences. Metaheuristic algorithms, in particular, have proven highly effective in complex optimization scenarios characterized by high dimensionality and intricate variable relationships. The Mountain Gazelle Optimizer (MGO) is notably effective but struggles to balance local search refinement and global space exploration, often leading to premature convergence and entrapment in local optima. This paper presents the Improved MGO (IMGO), which integrates three synergistic enhancements: dynamic chaos mapping using piecewise chaotic sequences to boost exploration diversity; Opposition-Based Learning (OBL) with adaptive, diversity-driven activation to speed up… More >

Mohammad Shokouhifar^1,*, Fakhrosadat Fanian², Mehdi Hosseinzadeh^3,4,*, Aseel Smerat^5,6, Kamal M. Othman⁷, Abdulfattah Noorwali⁷, Esam Y. O. Zafar⁷

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2026.073789 - 29 January 2026

Abstract Wireless Sensor Networks (WSNs) have become foundational in numerous real-world applications, ranging from environmental monitoring and industrial automation to healthcare systems and smart city development. As these networks continue to grow in scale and complexity, the need for energy-efficient, scalable, and robust communication protocols becomes more critical than ever. Metaheuristic algorithms have shown significant promise in addressing these challenges, offering flexible and effective solutions for optimizing WSN performance. Among them, the Grey Wolf Optimizer (GWO) algorithm has attracted growing attention due to its simplicity, fast convergence, and strong global search capabilities. Accordingly, this survey provides… More >

Ahmed Awad Mohamed¹, Eslam Abdelhakim Seyam^2,*, Ahmed R. Elsaeed³, Laith Abualigah⁴, Aseel Smerat^5,6, Ahmed M. AbdelMouty⁷, Hosam E. Refaat⁸

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073171 - 12 January 2026

Abstract In recent years, fog computing has become an important environment for dealing with the Internet of Things. Fog computing was developed to handle large-scale big data by scheduling tasks via cloud computing. Task scheduling is crucial for efficiently handling IoT user requests, thereby improving system performance, cost, and energy consumption across nodes in cloud computing. With the large amount of data and user requests, achieving the optimal solution to the task scheduling problem is challenging, particularly in terms of cost and energy efficiency. In this paper, we develop novel strategies to save energy consumption across… More >

Rekha Phadke¹, Abdul Lateef Haroon Phulara Shaik², Dayanidhi Mohapatra³, Doaa Sami Khafaga^4,*, Eman Abdullah Aldakheel⁴, N. Sathyanarayana⁵

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-26, 2026, DOI:10.32604/cmc.2025.067564 - 09 December 2025

Abstract Recently, the Internet of Things (IoT) technology has been utilized in a wide range of services and applications which significantly transforms digital ecosystems through seamless interconnectivity between various smart devices. Furthermore, the IoT plays a key role in multiple domains, including industrial automation, smart homes, and intelligent transportation systems. However, an increasing number of connected devices presents significant challenges related to efficient resource allocation and system responsiveness. To address these issue, this research proposes a Modified Walrus Optimization Algorithm (MWaOA) for effective resource management in smart IoT systems. In the proposed MWaOA, a crowding process… More >

Junxiang Li^1,2, Zhipeng Dong², Ben Han³, Jianqiao Chen³, Xinxin Zhang^1,2,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-19, 2026, DOI:10.32604/cmc.2025.070816 - 10 November 2025

Abstract Owing to their global search capabilities and gradient-free operation, metaheuristic algorithms are widely applied to a wide range of optimization problems. However, their computational demands become prohibitive when tackling high-dimensional optimization challenges. To effectively address these challenges, this study introduces cooperative metaheuristics integrating dynamic dimension reduction (DR). Building upon particle swarm optimization (PSO) and differential evolution (DE), the proposed cooperative methods C-PSO and C-DE are developed. In the proposed methods, the modified principal components analysis (PCA) is utilized to reduce the dimension of design variables, thereby decreasing computational costs. The dynamic DR strategy implements periodic… More >

Nisa Nacar Çıkan^*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3279-3327, 2025, DOI:10.32604/cmes.2025.065442 - 30 June 2025

Abstract This study addresses the critical challenge of reconfiguration in unbalanced power distribution networks (UPDNs), focusing on the complex 123-Bus test system. Three scenarios are investigated: (1) simultaneous power loss reduction and voltage profile improvement, (2) minimization of voltage and current unbalance indices under various operational cases, and (3) multi-objective optimization using Pareto front analysis to concurrently optimize voltage unbalance index, active power loss, and current unbalance index. Unlike previous research that oftensimplified system components, this work maintains all equipment, including capacitor banks, transformers, and voltage regulators, to ensure realistic results. The study evaluates twelve metaheuristic More >

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 >

Yunhan Ling¹, Huajun Peng², Yiqing Shi^1,*, Chao Xu¹, Jingzhen Yan¹, Jingjing Wang¹, Hui Ma^3,*

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 >

Oluwaseun Peter Ige^1,2, Keng Hoon Gan^1,*

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 >

Tareq Hamadneh^1,2, Khalid Kaabneh³, Omar Alssayed⁴, Kei Eguchi^5,*, Zeinab Monrazeri⁶, Mohammad Dehghani⁶

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.2, pp. 1725-1808, 2024, DOI:10.32604/cmes.2024.053236 - 27 September 2024

Abstract In this article, a novel metaheuristic technique named Far and Near Optimization (FNO) is introduced, offering versatile applications across various scientific domains for optimization tasks. The core concept behind FNO lies in integrating global and local search methodologies to update the algorithm population within the problem-solving space based on moving each member to the farthest and nearest member to itself. The paper delineates the theory of FNO, presenting a mathematical model in two phases: (i) exploration based on the simulation of the movement of a population member towards the farthest member from itself and (ii)… More >