Lishu Qin^*, Yu Gao, Xinyuan Lu

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074739 - 10 February 2026

Abstract With the rapid development of intelligent navigation technology, efficient and safe path planning for mobile robots has become a core requirement. To address the challenges of complex dynamic environments, this paper proposes an intelligent path planning framework based on grid map modeling. First, an improved Safe and Smooth A* (SSA*) algorithm is employed for global path planning. By incorporating obstacle expansion and corner-point optimization, the proposed SSA* enhances the safety and smoothness of the planned path. Then, a Partitioned Dynamic Window Approach (PDWA) is integrated for local planning, which is triggered when dynamic or sudden… More >

Shao-Qiang Ye^*, Azlan Mohd Zain, Yusliza Yusoff

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.073411 - 10 February 2026

Abstract Engineering optimization problems are often characterized by high dimensionality, constraints, and complex, multimodal landscapes. Traditional deterministic methods frequently struggle under such conditions, prompting increased interest in swarm intelligence algorithms. Among these, the Cuckoo Search (CS) algorithm stands out for its promising global search capabilities. However, it often suffers from premature convergence when tackling complex problems. To address this limitation, this paper proposes a Grouped Dynamic Adaptive CS (GDACS) algorithm. The enhancements incorporated into GDACS can be summarized into two key aspects. Firstly, a chaotic map is employed to generate initial solutions, leveraging the inherent randomness… More >

Chih-Chieh Chang^*, Khairul Izyan Bin Anuar, Yu-Hwa Liu

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.073177 - 10 February 2026

Abstract Small datasets are often challenging due to their limited sample size. This research introduces a novel solution to these problems: average linkage virtual sample generation (ALVSG). ALVSG leverages the underlying data structure to create virtual samples, which can be used to augment the original dataset. The ALVSG process consists of two steps. First, an average-linkage clustering technique is applied to the dataset to create a dendrogram. The dendrogram represents the hierarchical structure of the dataset, with each merging operation regarded as a linkage. Next, the linkages are combined into an average-based dataset, which serves as… More >

Su Zhang¹, Song Deng^1,*, Qingsheng Liu²

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.072777 - 10 February 2026

Abstract Accurate and reliable power system data are fundamental for critical operations such as grid monitoring, fault diagnosis, and load forecasting, underpinned by increasing intelligentization and digitalization. However, data loss and anomalies frequently compromise data integrity in practical settings, significantly impacting system operational efficiency and security. Most existing data recovery methods require complete datasets for training, leading to substantial data and computational demands and limited generalization. To address these limitations, this study proposes a missing data imputation model based on an improved Generative Adversarial Network (BAC-GAN). Within the BAC-GAN framework, the generator utilizes Bidirectional Long Short-Term… More >

Leyu Dai^1,2,3, Jindong Wang^1,2,3, Ming Zhou^1,2,3, Song Guo^1,2,3, Hengwei Zhang^1,2,3,*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.072449 - 10 February 2026

Abstract In recent years, with the rapid advancement of artificial intelligence, object detection algorithms have made significant strides in accuracy and computational efficiency. Notably, research and applications of Anchor-Free models have opened new avenues for real-time target detection in optical remote sensing images (ORSIs). However, in the realm of adversarial attacks, developing adversarial techniques tailored to Anchor-Free models remains challenging. Adversarial examples generated based on Anchor-Based models often exhibit poor transferability to these new model architectures. Furthermore, the growing diversity of Anchor-Free models poses additional hurdles to achieving robust transferability of adversarial attacks. This study presents… More >

Mehran Tarif¹, Mohammadhossein Homaei^2,*, Abbas Mirzaei³, Babak Nouri-Moghaddam³

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.071676 - 10 February 2026

Abstract The Routing Protocol for Low-power and Lossy Networks (RPL) is widely used in Internet of Things (IoT) systems, where devices usually have very limited resources. However, RPL still faces several problems, such as high energy usage, unstable links, and inefficient routing decisions, which reduce the overall network performance and lifetime. In this work, we introduce TABURPL, an improved routing method that applies Tabu Search (TS) to optimize the parent selection process. The method uses a combined cost function that considers Residual Energy, Transmission Energy, Distance to the Sink, Hop Count, Expected Transmission Count (ETX), and More >

Farhad Soleimanian Gharehchopogh^*, Keyvan Fattahi Rishakan

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.073808 - 29 January 2026

Abstract Optimization algorithms are crucial for solving NP-hard problems in engineering and computational sciences. Metaheuristic algorithms, in particular, have proven highly effective in complex optimization scenarios characterized by high dimensionality and intricate variable relationships. The Mountain Gazelle Optimizer (MGO) is notably effective but struggles to balance local search refinement and global space exploration, often leading to premature convergence and entrapment in local optima. This paper presents the Improved MGO (IMGO), which integrates three synergistic enhancements: dynamic chaos mapping using piecewise chaotic sequences to boost exploration diversity; Opposition-Based Learning (OBL) with adaptive, diversity-driven activation to speed up… More >

Xiaolu Wang^1,2, Haoyu Sun¹, Aiguo Wang¹, Xin Xia^3,*

Energy Engineering, Vol.123, No.2, 2026, DOI:10.32604/ee.2025.073991 - 27 January 2026

Abstract To address the issues of insufficient and imbalanced data samples in proton exchange membrane fuel cell (PEMFC) performance degradation prediction, this study proposes a data augmentation-based model to predict PEMFC performance degradation. Firstly, an improved generative adversarial network (IGAN) with adaptive gradient penalty coefficient is proposed to address the problems of excessively fast gradient descent and insufficient diversity of generated samples. Then, the IGAN is used to generate data with a distribution analogous to real data, thereby mitigating the insufficiency and imbalance of original PEMFC samples and providing the prediction model with training data rich More >

José Armando Santas Ciavatta, Juan Ramón Bermejo Higuera^*, Javier Bermejo Higuera, Juan Antonio Sicilia Montalvo, Tomás Sureda Riera, Jesús Pérez Melero

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.074566 - 12 January 2026

Abstract As artificial Intelligence (AI) continues to expand exponentially, particularly with the emergence of generative pre-trained transformers (GPT) based on a transformer’s architecture, which has revolutionized data processing and enabled significant improvements in various applications. This document seeks to investigate the security vulnerabilities detection in the source code using a range of large language models (LLM). Our primary objective is to evaluate the effectiveness of Static Application Security Testing (SAST) by applying various techniques such as prompt persona, structure outputs and zero-shot. To the selection of the LLMs (CodeLlama 7B, DeepSeek coder 7B, Gemini 1.5 Flash,… 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 >