Piotr Musznicki¹, Marek Turzyński¹, Lyu Guanghua², Ghulam E Mustafa Abro^3,*, Viola Gierszewska¹, Arsalan Muhammad Soomar¹, Syed Hadi Hussain Shah²

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.082254 - 13 August 2026

Abstract This paper presents an accurate and efficient methodology for parameter extraction in complex impedance models using Differential Evolution (DE), an evolutionary optimization technique. The proposed approach targets equivalent RLC circuit topologies and aims to match measured impedance characteristics across a wide frequency spectrum. By formulating the extraction process as a global optimization problem, DE enables precise identification of component values, even for high-order models with multiple resonances. The method is implemented in Python using open-source libraries, facilitating reproducibility and integration into broader modeling workflows. Validation is performed on both analytically derived resonant circuits and physically More >

Shu-Chuan Chu^1,2, Libin Fu², Jeng-Shyang Pan^1,2,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.084391 - 27 July 2026

Abstract The Animated Oat Optimization Algorithm (AOO) is a novel evolutionary algorithm inspired by the behavior of animated oats. This paper proposes a Competitive Parallel Animated Oat Optimization Algorithm (CPAOO) comprising two components. First, a parallel strategy is employed in which inter-subpopulation communication is triggered at predefined iteration thresholds to balance exploration and exploitation. Second, a grouped competition strategy with incentive mechanisms is introduced, enabling the prioritized evolution of superior individuals to enhance the algorithm’s efficiency. Furthermore, building on the Prediction Error Expansion (PEE) algorithm, this paper proposes a Dual-Layer PEE (DLPEE) algorithm for reversible digital More >

Yun Liu¹, Jinghua Zhao¹, Liang Ma¹, Zijie Huang^2,3,*, Lizhi Cai^2,3, Jianxin Ge^2,3

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.084179 - 23 July 2026

Abstract Automated library migration reduces refactoring costs but challenges traditional evolutionary algorithms, which often suffer from premature convergence and poor recall in sparse, complex API mapping spaces. To address this, we propose QIMIG, a multi-objective optimization framework integrating quantum-inspired encoding with quality-aware and greedy heuristic filtering. QIMIG utilizes a probabilistic Q-bit representation to maintain population diversity and avoid local optima. Simultaneously, its heuristic components leverage historical usage context to filter semantic noise and guide the search toward valid mappings. Evaluated on 9 real-world migration rules derived from 57,447 open-source projects, QIMIG statistically significantly outperforms state-of-the-art baselines More >

Mohammed A. El-Shorbagy¹, Anas Bouaouda^2,*, Fatma A. Hashim³

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.077765 - 27 May 2026

Abstract As real-world optimization problems become more complex, the development of sophisticated and robust algorithms has become essential. Consequently, researchers are focusing on advanced optimization methods that efficiently explore the feasible solution space. This involves designing new high-performance algorithms or enhancing existing meta-heuristic methods by integrating advanced evolutionary strategies. Barnacles Mating Optimizer (BMO) is an evolutionary-based meta-heuristic algorithm inspired by the mating behavior of barnacles, incorporating Hardy–Weinberg principles and the sperm-cast mechanism. Introduced in 2020, BMO has attracted significant attention and has been successfully applied across diverse fields due to its simple design, ease of implementation,… More >

Sicheng Ma¹, Lixiang Zhang^2,*, Guocai Chen³, Zeyu Dai³, Junru Zhu⁴, Wei Fang^1,*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.073572 - 10 February 2026

Abstract Community detection is a fundamental problem in network analysis for identifying densely connected node clusters, with successful applications in diverse fields like social networks, recommendation systems, biology, and cyberattack detection. Overlapping community detection refers to the case of a node belonging to multiple communities simultaneously, which is a much more meaningful and challenging task. Graph representation learning with Evolutionary Computation has been studied well in overlapping community detection to deal with complex network structures and characteristics. However, most of them focus on searching the entire solution space, which can be inefficient and lead to inadequate… More >

Leyu Zheng¹, Mingming Xiao^1,*, Yi Ren², Ke Li¹, Chang Sun¹

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072036 - 12 January 2026

Abstract In a wide range of engineering applications, complex constrained multi-objective optimization problems (CMOPs) present significant challenges, as the complexity of constraints often hampers algorithmic convergence and reduces population diversity. To address these challenges, we propose a novel algorithm named Constraint Intensity-Driven Evolutionary Multitasking (CIDEMT), which employs a two-stage, tri-task framework to dynamically integrates problem structure and knowledge transfer. In the first stage, three cooperative tasks are designed to explore the Constrained Pareto Front (CPF), the Unconstrained Pareto Front (UPF), and the ε-relaxed constraint boundary, respectively. A CPF-UPF relationship classifier is employed to construct a problem-type-aware… More >

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 >

Muhammad Waqar Khan^1,*, Adnan Ahmed Siddiqui¹, Syed Sajjad Hussain Rizvi²

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-15, 2026, DOI:10.32604/cmc.2025.070858 - 09 December 2025

Abstract The multi-objective optimization problems, especially in constrained environments such as power distribution planning, demand robust strategies for discovering effective solutions. This work presents the improved variant of the Multi-population Cooperative Constrained Multi-Objective Optimization (MCCMO) Algorithm, termed Adaptive Diversity Preservation (ADP). This enhancement is primarily focused on the improvement of constraint handling strategies, local search integration, hybrid selection approaches, and adaptive parameter control. The improved variant was experimented on with the RWMOP50 power distribution system planning benchmark. As per the findings, the improved variant outperformed the original MCCMO across the eleven performance metrics, particularly in terms… 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 >

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 >