Bhanu Talwar^1,*, Puneet Thapar¹, Tahani Alsubait², Mai Alduailij³, Ateeq Ur Rehman^4,*, Salil Bharany⁵

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.080977 - 15 June 2026

Abstract Wireless Sensor Networks (WSNs) play a vital role in smart city Internet of Things (IoT) applications, including environmental monitoring, intelligent transportation, and infrastructure management. However, limited battery capacity, uneven energy consumption, and inefficient clustering and routing mechanisms significantly reduce network lifetime, reliability, and scalability, especially in large-scale IoT deployments. Traditional routing protocols often rely on single-objective optimization or static clustering strategies, which fail to maintain long-term energy balance and stable communication performance. To address these challenges, this paper proposes iPAFAR, a Pareto-based multi-objective clustering and routing framework designed for IoT-enabled WSNs. The proposed model formulates… More >

Anjie Hu¹, Rui Zhao¹, Liu Tang^2,3,*, Jun Wang^2,3, Dong Liu¹

FDMP-Fluid Dynamics & Materials Processing, Vol.22, No.5, 2026, DOI:10.32604/fdmp.2026.080001 - 27 May 2026

Abstract Lithium-ion batteries are widely deployed in electric vehicles, yet their performance and safety are strongly constrained by elevated operating temperatures, which may accelerate degradation and, in extreme cases, trigger thermal runaway. This study numerically investigates the thermal performance of a Tesla valve-based cold plate for battery thermal management, with the aim of enhancing heat dissipation efficiency through multi-parameter collaborative optimization. An initial screening is conducted using orthogonal experimental design to evaluate the effects of shunt angle (30°–50°), number of unit pairs (3–7), channel asymmetry ratio (0–0.5), and branch channel width (2–4 mm) on maximum temperature… More >

Marcelo Dario Rodas Britez^1,*, Francesco Ghionda², Vasileios Tatsis³, Dimosthenis Ioannidis³

Energy Engineering, Vol.123, No.6, 2026, DOI:10.32604/ee.2026.078082 - 27 May 2026

Abstract Current energy systems are increasingly complex, considering multi-energy systems, the integration of non-programmable renewable energy sources, and the simultaneous evaluation of multiple evaluation objectives (i.e., costs vs carbon dioxide emissions). This complexity opens the opportunity to explore optimization algorithms as assistance for systematic and automatic management of energy systems. The implementation of a multi-energy system poses multiple challenges, including managing multiple energy vectors with different technologies applied across energy production, energy storage, and renewable energy sources. Also, multi-objective evaluation should be considered to manage reductions in costs and carbon dioxide emissions. Therefore, this paper proposes… More >

Yangjun Zhou¹, Chenying Yi¹, Wei Zhang¹, Juntao Pan^2,*, Ke Zhou¹, Weixiang Huang¹, Like Gao¹, Shan Li¹, Yuanchao Zhou³, Ling Li², Liwen Qin¹, Hongwen Wu⁴, Lijuan Yan²

Energy Engineering, Vol.123, No.6, 2026, DOI:10.32604/ee.2025.073787 - 27 May 2026

Abstract The increasing integration of distributed generation (DG) and energy storage systems (ESS) has significantly enhanced the flexibility and efficiency of distribution networks. However, the growing frequency of extreme weather events has exposed the vulnerability of distribution lines, posing serious challenges to the reliability and resilience of such systems. Existing DG and ESS planning models often neglect this vulnerability dimension, leading to suboptimal siting decisions and reduced system robustness. To address this issue, this paper proposes a comprehensive multi-objective optimization framework that coordinates the allocation of DG and ESS and explicitly incorporates line vulnerability under extreme… More >

Changtian Zhang¹, Qi Zhang^1,*, Shujin Qin², Xiwang Guo³, Bin Hu^4,*, Wenjie Luo¹

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.076620 - 08 May 2026

Abstract The increasing complexity of steel manufacturing and the rising demand for customized high-grade plates have intensified the need for efficient and defect-aware cutting optimization. In practical production, mother plates frequently contain multiple surface defects, and the cutting process is further constrained by delay-sensitive operations such as tool-change sequences and defect-tolerance requirements. To address these challenges, this study formulates the Defective Multi-Inventory Mother-Plate Two-Dimensional Cutting Stock Problem (DMMP-2CSP) as a multi-objective model that simultaneously maximizes cutting profit and minimizes tool changes under strict geometric and defect-avoidance constraints. We develop an Improved Multi-Objective Grey Wolf Optimizer (IMOGWO) More >

Wei Dong¹, Baoqi Guo², Weiwei Zhao², Hui Jian², Zhenzong He^2,*

Frontiers in Heat and Mass Transfer, Vol.24, No.2, 2026, DOI:10.32604/fhmt.2025.075848 - 30 April 2026

Abstract This study explores the design of a tapered cathode flow channel in a proton exchange membrane fuel cell (PEMFC), leveraging artificial intelligence and multi-objective optimization techniques to attain an optimal configuration. First, the influence of the channel height ratio and mass flow rate on PEMFC performance was systematically examined. The results reveal that decreasing the height ratio and increasing the mass flow rate lead to reduction in the standard deviation of current density, accompanied by a monotonic rise in pressure drop. The average current density initially rises before exhibiting a slight decline. Subsequently, a surrogate… More >

Ali S. Alghamdi^*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.079763 - 27 April 2026

Abstract The integration of wind-based DG introduces significant variability and uncertainty into the operation of distribution networks, which complicates the planning and decision-making process. This paper presents a dual-objective stochastic optimization framework for the optimal allocation of wind DG, considering dynamic network reconfiguration across multiple loading conditions. Probabilistic modeling of wind speed is integrated using the Weibull distribution and the associated wind power uncertainty is discretized through a scenario-based point estimation method. Variability in load is accounted for by considering multiple loading levels, and the integrated uncertainty space is constructed as the Cartesian product of wind… More >

Adwaa Mohammed Abdulmajeed¹, Duaa Abdul Rida Musa², Ola Abdul Hussain², Emad Kadum Njim³, Royal Madan^4,*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076622 - 09 April 2026

Abstract A data-driven optimization framework that integrates machine learning surrogate models, finite element analysis (FEA), and a multi-objective optimization algorithm is used in this study for developing thermoplastic elastomer (TPE) parts for aerospace applications. By using FEA simulations and experiments, a database of input design parameters (e.g., geometry and structural shape modifier) is generated. Afterwards, we train surrogate models (e.g., Gaussian Process Regression, neural networks) to approximate mappings from design space to performance space. Finally, we propose Pareto-optimal TPE designs using the surrogate embedded in a multi-objective optimization loop (such as NSGA-II or gradient-based methods). The… More >

Chin-Wen Liao^1,2,3, En-Shiuh Lin¹, Wei-Lun Huang^4,5,6, I-Chi Wang⁷, Bo-Siang Chen^8,*, Wei-Sho Ho^1,2,9,*

Journal of Polymer Materials, Vol.43, No.1, 2026, DOI:10.32604/jpm.2026.076807 - 03 April 2026

Abstract The demand for extended electric vehicle (EV) range necessitates advanced lightweighting strategies. This study introduces a materials genome approach, augmented by machine learning (ML), for optimizing lightweight composite designs for EVs. A comprehensive materials genome database was developed, encompassing composites based on carbon, glass, and natural fibers. This database systematically records critical parameters such as mechanical properties, density, cost, and environmental impact. Machine learning models, including Random Forest, Support Vector Machines, and Artificial Neural Networks, were employed to construct a predictive system for material performance. Subsequent material composition optimization was performed using a multi-objective genetic More >

Yao-Hsin Chou¹, Cheng-Yen Hua¹, Ru-Wei Tseng¹, Shu-Yu Kuo^2,*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2025.075705 - 12 March 2026

Abstract The rapid growth of mobile and Internet of Things (IoT) applications in dense urban environments places stringent demands on future Beyond 5G (B5G) or Beyond 6G (B6G) networks, which must ensure high Quality of Service (QoS) while maintaining cost-efficiency and sustainable deployment. Traditional strategies struggle with complex 3D propagation, building penetration loss, and the balance between coverage and infrastructure cost. To address this challenge, this study presents the first application of a Global-best Guided Quantum-inspired Tabu Search with Quantum-Not Gate (GQTS-QNG) framework for 3D base-station deployment optimization. The problem is formulated as a multi-objective model… More >