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 >

Bohui Li¹, Bin Wang¹, Linjie Wu¹, Xingjuan Cai^1,*, Maoqing Zhang^2,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-28, 2026, DOI:10.32604/cmc.2025.070592 - 09 December 2025

Abstract Federated Learning (FL) provides an effective framework for efficient processing in vehicular edge computing. However, the dynamic and uncertain communication environment, along with the performance variations of vehicular devices, affect the distribution and uploading processes of model parameters. In FL-assisted Internet of Vehicles (IoV) scenarios, challenges such as data heterogeneity, limited device resources, and unstable communication environments become increasingly prominent. These issues necessitate intelligent vehicle selection schemes to enhance training efficiency. Given this context, we propose a new scenario involving FL-assisted IoV systems under dynamic and uncertain communication conditions, and develop a dynamic interval multi-objective More >

Abu Tayab^1,*, Yanwen Li¹, Ahmad Syed², Ghanshyam G. Tejani^3,4,*, Doaa Sami Khafaga⁵, El-Sayed M. El-kenawy⁶, Amel Ali Alhussan⁷, Marwa M. Eid^8,9

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-27, 2026, DOI:10.32604/cmc.2025.070583 - 09 December 2025

Abstract Autonomous connected vehicles (ACV) involve advanced control strategies to effectively balance safety, efficiency, energy consumption, and passenger comfort. This research introduces a deep reinforcement learning (DRL)-based car-following (CF) framework employing the Deep Deterministic Policy Gradient (DDPG) algorithm, which integrates a multi-objective reward function that balances the four goals while maintaining safe policy learning. Utilizing real-world driving data from the highD dataset, the proposed model learns adaptive speed control policies suitable for dynamic traffic scenarios. The performance of the DRL-based model is evaluated against a traditional model predictive control-adaptive cruise control (MPC-ACC) controller. Results show that the… More >

Junjun Ren¹, Guoqiang Chen², Zheng-Yi Chai³, Dong Yuan^4,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-26, 2026, DOI:10.32604/cmc.2025.068795 - 10 November 2025

Abstract Vehicle Edge Computing (VEC) and Cloud Computing (CC) significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrained onboard devices to nearby Roadside Unit (RSU), thereby achieving lower delay and energy consumption. However, due to the limited storage capacity and energy budget of RSUs, it is challenging to meet the demands of the highly dynamic Internet of Vehicles (IoV) environment. Therefore, determining reasonable service caching and computation offloading strategies is crucial. To address this, this paper proposes a joint service caching scheme for cloud-edge collaborative IoV computation offloading. By… 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 >

Zheng Yao^*, Puqing Chang

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-18, 2026, DOI:10.32604/cmc.2025.068248 - 10 November 2025

Abstract As Internet of Things (IoT) applications expand, Mobile Edge Computing (MEC) has emerged as a promising architecture to overcome the real-time processing limitations of mobile devices. Edge-side computation offloading plays a pivotal role in MEC performance but remains challenging due to complex task topologies, conflicting objectives, and limited resources. This paper addresses high-dimensional multi-objective offloading for serial heterogeneous tasks in MEC. We jointly consider task heterogeneity, high-dimensional objectives, and flexible resource scheduling, modeling the problem as a Many-objective optimization. To solve it, we propose a flexible framework integrating an improved cooperative co-evolutionary algorithm based on More >

Rytis Maskeliūnas¹, Robertas Damaševičius^1,*, Audrius Kulikajevas¹, Kipras Pribuišis², Nora Ulozaitė-Stanienė², Virgilijus Uloza²

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 4203-4223, 2025, DOI:10.32604/cmes.2025.072790 - 23 December 2025

Abstract This study introduces a novel voice cloning framework driven by Mordukhovich Subdifferential Optimization (MSO) to address the complex multi-objective challenges of pathological speech synthesis in under-resourced Lithuanian language with unique phonemes not present in most pre-trained models. Unlike existing voice synthesis models that often optimize for a single objective or are restricted to major languages, our approach explicitly balances four competing criteria: speech naturalness, speaker similarity, computational efficiency, and adaptability to pathological voice patterns. We evaluate four model configurations combining Lithuanian and English encoders, synthesizers, and vocoders. The hybrid model (English encoder, Lithuanian synthesizer, English More >

Ramazan Özkan^1,2, Mustafa Serdar Genç^1,3,*, İlker Kayali^1,4,5

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3349-3380, 2025, DOI:10.32604/cmes.2025.072519 - 23 December 2025

Abstract The optimization of turbine blades is crucial in improving the efficiency of wind energy systems and developing clean energy production models. This paper presented a novel approach to the structural design of small-scale turbine blades using the Artificial Bee Colony (ABC) Algorithm based on the stochastic method to optimize both mass and cost (objective functions). The study used computational fluid dynamics (CFD) and structural analysis to consider the fluid-structure interaction. The optimization algorithm defined several variables: structural constraints, the type of composite material, and the number of composite layers to form a mathematical model. The More >

Abdullrahman A. Al-Shamma’a¹, Hassan M. Hussein Farh^1,*, Ridwan Taiwo², Al-Wesabi Ibrahim³, Abdulrhman Alshaabani¹, Saad Mekhilef ⁴, Mohamed A. Mohamed^5,6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 1303-1347, 2025, DOI:10.32604/cmes.2025.071302 - 26 November 2025

Abstract Optimal sizing and allocation of distributed generators (DGs) have become essential computational challenges in improving the performance, efficiency, and reliability of electrical distribution networks. Despite extensive research, existing approaches often face algorithmic limitations such as slow convergence, premature stagnation in local minima, or suboptimal accuracy in determining optimal DG placement and capacity. This study presents a comprehensive scientometric and systematic review of global research focused on computer-based modelling and algorithmic optimization for renewable DG sizing and placement. It integrates both quantitative and qualitative analyses of the scholarly landscape, mapping influential research domains, co-authorship structures, the More >

Shuxun Li^1,2, Xinhao Liu^1,2,*, Yu Zhang^1,2, Yu Zhao^1,2

FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.10, pp. 2495-2526, 2025, DOI:10.32604/fdmp.2025.068674 - 30 October 2025

Abstract Hydraulic control valves, positioned at the terminus of pipe networks, are critical for regulating flow and pressure, thereby ensuring the operational safety and efficiency of pipeline systems. However, conventional valve designs often struggle to maintain effective regulation across a wide range of system pressures. To address this limitation, this study introduces a novel Pilot hydraulic valves specifically engineered for enhanced dynamic performance and precise regulation under variable pressure conditions. Building upon prior experimental findings, the proposed design integrates a high-fidelity simulation framework and a surrogate model-based optimization strategy. The study begins by formulating a comprehensive… More >