Asal Jameel Khudhair^#, Amenah Dahim Abbood^#,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-31, 2026, DOI:10.32604/cmc.2025.068553 - 10 November 2025

Abstract Community detection is one of the most fundamental applications in understanding the structure of complicated networks. Furthermore, it is an important approach to identifying closely linked clusters of nodes that may represent underlying patterns and relationships. Networking structures are highly sensitive in social networks, requiring advanced techniques to accurately identify the structure of these communities. Most conventional algorithms for detecting communities perform inadequately with complicated networks. In addition, they miss out on accurately identifying clusters. Since single-objective optimization cannot always generate accurate and comprehensive results, as multi-objective optimization can. Therefore, we utilized two objective functions… More >

Erping Song^1,*, Zipin Yao²

Energy Engineering, Vol.122, No.12, pp. 5129-5147, 2025, DOI:10.32604/ee.2025.063827 - 27 November 2025

Abstract Wind farm layout optimization is a critical challenge in renewable energy development, especially in regions with complex terrain. Micro-siting of wind turbines has a significant impact on the overall efficiency and economic viability of wind farm, where the wake effect, wind speed, types of wind turbines, etc., have an impact on the output power of the wind farm. To solve the optimization problem of wind farm layout under complex terrain conditions, this paper proposes wind turbine layout optimization using different types of wind turbines, the aim is to reduce the influence of the wake effect… More >

Thomas Hanne^1,*, Mohammad Jahani Moghaddam²

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4203-4236, 2025, DOI:10.32604/cmc.2025.068087 - 23 October 2025

Abstract Multi-Objective Evolutionary Algorithms (MOEAs) have significantly advanced the domain of Multi-Objective Optimization (MOO), facilitating solutions for complex problems with multiple conflicting objectives. This review explores the historical development of MOEAs, beginning with foundational concepts in multi-objective optimization, basic types of MOEAs, and the evolution of Pareto-based selection and niching methods. Further advancements, including decom-position-based approaches and hybrid algorithms, are discussed. Applications are analyzed in established domains such as engineering and economics, as well as in emerging fields like advanced analytics and machine learning. The significance of MOEAs in addressing real-world problems is emphasized, highlighting their More >

Siqi Zhang, Shengda Pan^*

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4655-4676, 2025, DOI:10.32604/cmc.2025.064864 - 30 July 2025

Abstract Infrared images typically exhibit diverse backgrounds, each potentially containing noise and target-like interference elements. In complex backgrounds, infrared small targets are prone to be submerged by background noise due to their low pixel proportion and limited available features, leading to detection failure. To address this problem, this paper proposes an Attention Shift-Invariant Cross-Evolutionary Feature Fusion Network (ASCFNet) tailored for the detection of infrared weak and small targets. The network architecture first designs a Multidimensional Lightweight Pixel-level Attention Module (MLPA), which alleviates the issue of small-target feature suppression during deep network propagation by combining channel reshaping,… More >

Siti Ramadhani^1,2, Lestari Handayani², Theam Foo Ng³, Sumayyah Dzulkifly¹, Roziana Ariffin^4,5, Haldi Budiman⁶, Shir Li Wang^1,7,*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 2711-2765, 2025, DOI:10.32604/cmes.2025.062709 - 30 June 2025

Abstract In recent years, feature selection (FS) optimization of high-dimensional gene expression data has become one of the most promising approaches for cancer prediction and classification. This work reviews FS and classification methods that utilize evolutionary algorithms (EAs) for gene expression profiles in cancer or medical applications based on research motivations, challenges, and recommendations. Relevant studies were retrieved from four major academic databases–IEEE, Scopus, Springer, and ScienceDirect–using the keywords ‘cancer classification’, ‘optimization’, ‘FS’, and ‘gene expression profile’. A total of 67 papers were finally selected with key advancements identified as follows: (1) The majority of papers… More > Graphic Abstract

Wenbing Wang^*, Yongwen Liu

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 763-790, 2025, DOI:10.32604/cmc.2025.065179 - 09 June 2025

Abstract Multi-firmware comparison techniques can improve efficiency when auditing firmwares in bulk. However, the problem of matching functions between multiple firmwares has not been studied before. This paper proposes a multi-firmware comparison method based on evolutionary algorithms and trusted base points. We first model the multi-firmware comparison as a multi-sequence matching problem. Then, we propose an adaptation function and a population generation method based on trusted base points. Finally, we apply an evolutionary algorithm to find the optimal result. At the same time, we design the similarity of matching results as an evaluation metric to measure More >

Chen Wang^1,2, Tiezheng Guo¹, Qingwen Yang¹, Yanyi Liu¹, Jiawei Tang¹, Yingyou Wen^1,2,*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5561-5574, 2025, DOI:10.32604/cmc.2025.063676 - 19 May 2025

Abstract Primary light chain amyloidosis is a rare hematologic disease with multi-organ involvement. Nearly one-third of patients with amyloidosis experience five or more consultations before diagnosis, which may lead to a poor prognosis due to delayed diagnosis. Early risk prediction based on artificial intelligence is valuable for clinical diagnosis and treatment of amyloidosis. For this disease, we propose an Evolutionary Neural Architecture Searching (ENAS) based risk prediction model, which achieves high-precision early risk prediction using physical examination data as a reference factor. To further enhance the value of clinic application, we designed a natural language-based interpretable… More >

Hayder Faeq Alhashimi¹, Mhd Nour Hindia¹, Kaharudin Dimyati^1,*, Effariza Binti Hanafi¹, Feras Zen Alden², Faizan Qamar³, Quang Ngoc Nguyen^4,5,*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 3585-3622, 2025, DOI:10.32604/cmc.2025.062867 - 19 May 2025

Abstract The forthcoming 6G wireless networks have great potential for establishing AI-based networks that can enhance end-to-end connection and manage massive data of real-time networks. Artificial Intelligence (AI) advancements have contributed to the development of several innovative technologies by providing sophisticated specific AI mathematical models such as machine learning models, deep learning models, and hybrid models. Furthermore, intelligent resource management allows for self-configuration and autonomous decision-making capabilities of AI methods, which in turn improves the performance of 6G networks. Hence, 6G networks rely substantially on AI methods to manage resources. This paper comprehensively surveys the recent… More >

Song Gao, Shixin Liu^*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5623-5641, 2025, DOI:10.32604/cmc.2025.058334 - 19 May 2025

Abstract With the development of economic globalization, distributed manufacturing is becoming more and more prevalent. Recently, integrated scheduling of distributed production and assembly has captured much concern. This research studies a distributed flexible job shop scheduling problem with assembly operations. Firstly, a mixed integer programming model is formulated to minimize the maximum completion time. Secondly, a Q-learning-assisted co-evolutionary algorithm is presented to solve the model: (1) Multiple populations are developed to seek required decisions simultaneously; (2) An encoding and decoding method based on problem features is applied to represent individuals; (3) A hybrid approach of heuristic… More >

Mohammed A. El-Shorbagy¹, Anas Bouaouda^2,*, Laith Abualigah^3,4, Fatma A. Hashim^5,6

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 >