Ahmad Zia¹, Nazia Azim², Bekarystankyzy Akbayan³, Khalid J. Alzahrani⁴, Ateeq Ur Rehman^5,*, Faheem Ullah Khan⁶, Nouf Al-Kahtani⁷, Hend Khalid Alkahtani^8,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073818 - 12 January 2026

Abstract The cloud-fog computing paradigm has emerged as a novel hybrid computing model that integrates computational resources at both fog nodes and cloud servers to address the challenges posed by dynamic and heterogeneous computing networks. Finding an optimal computational resource for task offloading and then executing efficiently is a critical issue to achieve a trade-off between energy consumption and transmission delay. In this network, the task processed at fog nodes reduces transmission delay. Still, it increases energy consumption, while routing tasks to the cloud server saves energy at the cost of higher communication delay. Moreover, the… More >

Feng Lv^*, Huili Chu, Cheng Yang, Jiajie Zhang

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072915 - 12 January 2026

Abstract To address the issue that hybrid flow shop production struggles to handle order disturbance events, a dynamic scheduling model was constructed. The model takes minimizing the maximum makespan, delivery time deviation, and scheme deviation degree as the optimization objectives. An adaptive dynamic scheduling strategy based on the degree of order disturbance is proposed. An improved multi-objective Grey Wolf (IMOGWO) optimization algorithm is designed by combining the “job-machine” two-layer encoding strategy, the timing-driven two-stage decoding strategy, the opposition-based learning initialization population strategy, the POX crossover strategy, the dual-operation dynamic mutation strategy, and the variable neighborhood search… More >

Olanrewaju Lawrence Abraham^1,2,*, Md Asri Ngadi¹, Johan Bin Mohamad Sharif¹, Mohd Kufaisal Mohd Sidik¹

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072279 - 12 January 2026

Abstract Task scheduling in cloud computing is a multi-objective optimization problem, often involving conflicting objectives such as minimizing execution time, reducing operational cost, and maximizing resource utilization. However, traditional approaches frequently rely on single-objective optimization methods which are insufficient for capturing the complexity of such problems. To address this limitation, we introduce MDMOSA (Multi-objective Dwarf Mongoose Optimization with Simulated Annealing), a hybrid that integrates multi-objective optimization for efficient task scheduling in Infrastructure-as-a-Service (IaaS) cloud environments. MDMOSA harmonizes the exploration capabilities of the biologically inspired Dwarf Mongoose Optimization (DMO) with the exploitation strengths of Simulated Annealing (SA), 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 >

Hongyu Wang¹, Wenwu Cui¹, Kai Cui¹, Zixuan Meng^2,*, Bin Li², Wei Zhang¹, Wenwen Li¹

Energy Engineering, Vol.123, No.1, 2026, DOI:10.32604/ee.2025.069576 - 27 December 2025

Abstract To achieve low-carbon regulation of electric vehicle (EV) charging loads under the “dual carbon” goals, this paper proposes a coordinated scheduling strategy that integrates dynamic carbon factor prediction and multi-objective optimization. First, a dual-convolution enhanced improved Crossformer prediction model is constructed, which employs parallel 1 × 1 global and 3 × 3 local convolution modules (Integrated Convolution Block, ICB) for multi-scale feature extraction, combined with an Adaptive Spectral Block (ASB) to enhance time-series fluctuation modeling. Based on high-precision predictions, a carbon-electricity cost joint optimization model is further designed to balance economic, environmental, and grid-friendly objectives.… More > Graphic Abstract

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 >