Malvinder Singh Bali¹, Weiwei Jiang^2,*, Saurav Verma³, Kanwalpreet Kour⁴, Ashwini Rao³

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-19, 2026, DOI:10.32604/cmc.2025.070866 - 09 December 2025

Abstract In recent years, Blockchain Technology has become a paradigm shift, providing Transparent, Secure, and Decentralized platforms for diverse applications, ranging from Cryptocurrency to supply chain management. Nevertheless, the optimization of blockchain networks remains a critical challenge due to persistent issues such as latency, scalability, and energy consumption. This study proposes an innovative approach to Blockchain network optimization, drawing inspiration from principles of biological evolution and natural selection through evolutionary algorithms. Specifically, we explore the application of genetic algorithms, particle swarm optimization, and related evolutionary techniques to enhance the performance of blockchain networks. The proposed methodologies More >

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 >

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 >

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

Chen Zhang¹, Liming Liu¹, Yufei Yang¹, Yu Sun¹, Jiaxu Ning², Yu Zhang³, Changsheng Zhang^1,4,*, Ying Guo⁴

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5201-5223, 2024, DOI:10.32604/cmc.2024.050863 - 20 June 2024

Abstract The flying foxes optimization (FFO) algorithm, as a newly introduced metaheuristic algorithm, is inspired by the survival tactics of flying foxes in heat wave environments. FFO preferentially selects the best-performing individuals. This tendency will cause the newly generated solution to remain closely tied to the candidate optimal in the search area. To address this issue, the paper introduces an opposition-based learning-based search mechanism for FFO algorithm (IFFO). Firstly, this paper introduces niching techniques to improve the survival list method, which not only focuses on the adaptability of individuals but also considers the population’s crowding degree More >

Leila Safari-Monjeghtapeh¹, Mansour Esmaeilpour^2,*

Computer Systems Science and Engineering, Vol.48, No.3, pp. 705-721, 2024, DOI:10.32604/csse.2024.044892 - 20 May 2024

Abstract Path-based clustering algorithms typically generate clusters by optimizing a benchmark function. Most optimization methods in clustering algorithms often offer solutions close to the general optimal value. This study achieves the global optimum value for the criterion function in a shorter time using the minimax distance, Maximum Spanning Tree “MST”, and meta-heuristic algorithms, including Genetic Algorithm “GA” and Particle Swarm Optimization “PSO”. The Fast Path-based Clustering “FPC” algorithm proposed in this paper can find cluster centers correctly in most datasets and quickly perform clustering operations. The FPC does this operation using MST, the minimax distance, and… More >

Hassen Louati^1,2, Ali Louati^3,*, Elham Kariri³, Slim Bechikh²

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2519-2547, 2024, DOI:10.32604/cmes.2023.030806 - 15 December 2023

Abstract Recent developments in Computer Vision have presented novel opportunities to tackle complex healthcare issues, particularly in the field of lung disease diagnosis. One promising avenue involves the use of chest X-Rays, which are commonly utilized in radiology. To fully exploit their potential, researchers have suggested utilizing deep learning methods to construct computer-aided diagnostic systems. However, constructing and compressing these systems presents a significant challenge, as it relies heavily on the expertise of data scientists. To tackle this issue, we propose an automated approach that utilizes an evolutionary algorithm (EA) to optimize the design and compression More >

Shehab Abdulhabib Alzaeemi¹, Kim Gaik Tay^1,*, Audrey Huong¹, Saratha Sathasivam², Majid Khan bin Majahar Ali²

Computer Systems Science and Engineering, Vol.47, No.1, pp. 1163-1184, 2023, DOI:10.32604/csse.2023.038912 - 26 May 2023

Abstract Radial Basis Function Neural Network (RBFNN) ensembles have long suffered from non-efficient training, where incorrect parameter settings can be computationally disastrous. This paper examines different evolutionary algorithms for training the Symbolic Radial Basis Function Neural Network (SRBFNN) through the behavior’s integration of satisfiability programming. Inspired by evolutionary algorithms, which can iteratively find the near-optimal solution, different Evolutionary Algorithms (EAs) were designed to optimize the producer output weight of the SRBFNN that corresponds to the embedded logic programming 2Satisfiability representation (SRBFNN-2SAT). The SRBFNN’s objective function that corresponds to Satisfiability logic programming can be minimized by different… More >

Uzma Afzal¹, Tariq Mahmood², Ali Mustafa Qamar^3,*, Ayaz H. Khan^4,5

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6251-6267, 2023, DOI:10.32604/cmc.2023.037653 - 29 April 2023

Abstract A complex Laboratory Developed Test (LDT) is a clinical test developed within a single laboratory. It is typically configured from many feature constraints from clinical repositories, which are part of the existing Laboratory Information Management System (LIMS). Although these clinical repositories are automated, support for managing patient information with test results of an LDT is also integrated within the existing LIMS. Still, the support to configure LDTs design needs to be made available even in standard LIMS packages. The manual configuration of LDTs is a complex process and can generate configuration inconsistencies because many constraints… More >

Mohamed Ali Rakrouki^1,2,*, Abeer Aljohani¹, Nawaf Alharbe¹, Abdelaziz Berrais², Talel Ladhari²

Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 1119-1134, 2023, DOI:10.32604/iasc.2023.028604 - 06 June 2022

Abstract In this paper, we consider the problem of minimizing the total tardiness in a deterministic two-machine permutation flowshop scheduling problem subject to release dates of jobs and known unavailability periods of machines. The theoretical and practical importance of minimizing tardiness in flowshop scheduling environment has motivated us to investigate and solve this interested two-machine scheduling problem. Methods that solve this important optimality criterion in flowshop environment are mainly heuristics. In fact, despite the -hardness in the strong sense of the studied problem, to the best of our knowledge there are no approximate algorithms (constructive heuristics… More >