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 >

Wulfran Fendzi Mbasso^1,2, Idriss Dagal³, Manish Kumar Singla^4,5,*, Muhammad Suhail Shaikh⁶, Aseel Smerat⁷, Abdullah Mohammed Al Fatais^8,9, Ali Saeed Almufih^8,9, Rabia Emhamed Al Mamlookol^10,11

Energy Engineering, Vol.123, No.4, 2026, DOI:10.32604/ee.2026.072751 - 27 March 2026

Abstract Photovoltaic (PV) systems in the field operate under complex, uncertain conditions rapid irradiance ramps, partial shading, temperature swings, surface soiling, and weak-grid disturbances including off-nominal frequency and voltage distortion that degrade energy yield and power quality. We propose a drift-aware, power-quality-constrained MPPT framework that co-optimizes MPPT, PLL, and current-loop gains under stochastic frequency drift, while enforcing IEEE-519 limits (per-order I_h/I_L and TDD) during optimization. Unlike energy-only or THD-only methods, the design target integrates PQ constraints into the objective and is validated across calibrated drift scenarios with explicit per-order and TDD reporting. Operating scenarios are calibrated to… More > Graphic Abstract

Le Thi Hong Van^1,*, Le Duc Thuan¹, Pham Van Huong¹, Nguyen Hieu Minh²

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.075027 - 10 February 2026

Abstract Optimizing convolutional neural networks (CNNs) for IoT attack detection remains a critical yet challenging task due to the need to balance multiple performance metrics beyond mere accuracy. This study proposes a unified and flexible optimization framework that leverages metaheuristic algorithms to automatically optimize CNN configurations for IoT attack detection. Unlike conventional single-objective approaches, the proposed method formulates a global multi-objective fitness function that integrates accuracy, precision, recall, and model size (speed/model complexity penalty) with adjustable weights. This design enables both single-objective and weighted-sum multi-objective optimization, allowing adaptive selection of optimal CNN configurations for diverse deployment… More >

Marwa M. Eid^1,2,*, Amel Ali Alhussan³, Ebrahim A. Mattar⁴, Nima Khodadadi^5,*, El-Sayed M. El-Kenawy^6,7

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.073555 - 29 January 2026

Abstract Accurate prediction of concrete compressive strength is fundamental for optimizing mix designs, improving material utilization, and ensuring structural safety in modern construction. Traditional empirical methods often fail to capture the non-linear relationships among concrete constituents, especially with the growing use of supplementary cementitious materials and recycled aggregates. This study presents an integrated machine learning framework for concrete strength prediction, combining advanced regression models—namely CatBoost—with metaheuristic optimization algorithms, with a particular focus on the Somersaulting Spider Optimizer (SSO). A comprehensive dataset encompassing diverse mix proportions and material types was used to evaluate baseline machine learning models,… More >

Qun Song¹, Chao Gao¹, Han Wu¹, Zhiheng Rao¹, Huafeng Qin^1,*, Simon Fong^1,2,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-49, 2026, DOI:10.32604/cmc.2025.070918 - 09 December 2025

Abstract Metaheuristic algorithms, renowned for strong global search capabilities, are effective tools for solving complex optimization problems and show substantial potential in e-Health applications. This review provides a systematic overview of recent advancements in metaheuristic algorithms and highlights their applications in e-Health. We selected representative algorithms published between 2019 and 2024, and quantified their influence using an entropy-weighted method based on journal impact factors and citation counts. CThe Harris Hawks Optimizer (HHO) demonstrated the highest early citation impact. The study also examined applications in disease prediction models, clinical decision support, and intelligent health monitoring. Notably, the More >

Raja Masadeh¹, Omar Almomani^2,*, Abdullah Zaqebah¹, Shayma Masadeh³, Kholoud Alshqurat³, Ahmad Sharieh⁴, Nesreen Alsharman⁵

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3709-3737, 2025, DOI:10.32604/cmc.2025.066797 - 23 September 2025

Abstract This research presents a novel nature-inspired metaheuristic optimization algorithm, called the Narwhale Optimization Algorithm (NWOA). The algorithm draws inspiration from the foraging and prey-hunting strategies of narwhals, “unicorns of the sea”, particularly the use of their distinctive spiral tusks, which play significant roles in hunting, searching prey, navigation, echolocation, and complex social interaction. Particularly, the NWOA imitates the foraging strategies and techniques of narwhals when hunting for prey but focuses mainly on the cooperative and exploratory behavior shown during group hunting and in the use of their tusks in sensing and locating prey under the… More >

Anis Ben Ghorbal^1,*, Azedine Grine¹, Marwa M. Eid^2,3,*, El-Sayed M. El-Kenawy^4,5

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 2001-2028, 2025, DOI:10.32604/cmes.2025.068212 - 31 August 2025

Abstract Preterm birth remains a leading cause of neonatal complications and highlights the need for early and accurate prediction techniques to improve both fetal and maternal health outcomes. This study introduces a hybrid approach integrating Long Short-Term Memory (LSTM) networks with the Hybrid Greylag Goose and Particle Swarm Optimization (GGPSO) algorithm to optimize preterm birth classification using Electrohysterogram signals. The dataset consists of 58 samples of 1000-second-long Electrohysterogram recordings, capturing key physiological features such as contraction patterns, entropy, and statistical variations. Statistical analysis and feature selection methods are applied to identify the most relevant predictors and More > Graphic Abstract

Yuanfei Wei^1,2, Panpan Song³, Qifang Luo^3,4,*, Yongquan Zhou^1,2,3,4

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1235-1265, 2025, DOI:10.32604/cmc.2025.066527 - 29 August 2025

Abstract The combined heat and power economic dispatch (CHPED) problem is a highly intricate energy dispatch challenge that aims to minimize fuel costs while adhering to various constraints. This paper presents a hybrid differential evolution (DE) algorithm combined with an improved equilibrium optimizer (DE-IEO) specifically for the CHPED problem. The DE-IEO incorporates three enhancement strategies: a chaotic mechanism for initializing the population, an improved equilibrium pool strategy, and a quasi-opposite based learning mechanism. These strategies enhance the individual utilization capabilities of the equilibrium optimizer, while differential evolution boosts local exploitation and escape capabilities. The IEO enhances… More >

Ghada Atteia^1,*, Mohammed Dabboor², Konstantinos Karantzalos³, Maali Alabdulhafith¹

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1747-1767, 2025, DOI:10.32604/cmc.2025.063363 - 09 June 2025

Abstract This study explores the integration of Synthetic Aperture Radar (SAR) imagery with deep learning and metaheuristic feature optimization techniques for enhanced oil spill detection. This study proposes a novel hybrid approach for oil spill detection. The introduced approach integrates deep transfer learning with the metaheuristic Binary Harris Hawk optimization (BHHO) and Principal Component Analysis (PCA) for improved feature extraction and selection from input SAR imagery. Feature transfer learning of the MobileNet convolutional neural network was employed to extract deep features from the SAR images. The BHHO and PCA algorithms were implemented to identify subsets of… More >

Elif Varol Altay, Osman Altay, Yusuf Özçevik^*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 1039-1094, 2024, DOI:10.32604/cmes.2023.029404 - 30 December 2023

Abstract Real-world engineering design problems with complex objective functions under some constraints are relatively difficult problems to solve. Such design problems are widely experienced in many engineering fields, such as industry, automotive, construction, machinery, and interdisciplinary research. However, there are established optimization techniques that have shown effectiveness in addressing these types of issues. This research paper gives a comparative study of the implementation of seventeen new metaheuristic methods in order to optimize twelve distinct engineering design issues. The algorithms used in the study are listed as: transient search optimization (TSO), equilibrium optimizer (EO), grey wolf optimizer… More >