R. Saravanan^1,*, R. Muthaiah¹, A. Rajesh²

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2339-2356, 2024, DOI:10.32604/cmes.2023.030898 - 15 December 2023

Abstract This study develops an Enhanced Threshold Based Energy Detection approach (ETBED) for spectrum sensing in a cognitive radio network. The threshold identification method is implemented in the received signal at the secondary user based on the square law. The proposed method is implemented with the signal transmission of multiple outputs-orthogonal frequency division multiplexing. Additionally, the proposed method is considered the dynamic detection threshold adjustments and energy identification spectrum sensing technique in cognitive radio systems. In the dynamic threshold, the signal ratio-based threshold is fixed. The threshold is computed by considering the Modified Black Widow Optimization… More > Graphic Abstract

Thi-Kien Dao¹, Trong-The Nguyen^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2201-2237, 2024, DOI:10.32604/cmes.2023.029880 - 15 December 2023

Abstract Localization or positioning scheme in Wireless sensor networks (WSNs) is one of the most challenging and fundamental operations in various monitoring or tracking applications because the network deploys a large area and allocates the acquired location information to unknown devices. The metaheuristic approach is one of the most advantageous ways to deal with this challenging issue and overcome the disadvantages of the traditional methods that often suffer from computational time problems and small network deployment scale. This study proposes an enhanced whale optimization algorithm that is an advanced metaheuristic algorithm based on the siege mechanism… More >

Basma Mohamed^1,*, Linda Mohaisen², Mohammed Amin¹

Intelligent Automation & Soft Computing, Vol.38, No.1, pp. 19-34, 2023, DOI:10.32604/iasc.2023.031947 - 26 January 2024

Abstract In this paper, we consider the NP-hard problem of finding the minimum dominant resolving set of graphs. A vertex set B of a connected graph G resolves G if every vertex of G is uniquely identified by its vector of distances to the vertices in B. A resolving set is dominating if every vertex of G that does not belong to B is a neighbor to some vertices in B. The dominant metric dimension of G is the cardinality number of the minimum dominant resolving set. The dominant metric dimension is computed by a binary version of the Archimedes optimization… More >

Xiang Wang¹, Liangsa Wang^2,*, Han Li¹, Yibin Guo¹

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 2935-2969, 2023, DOI:10.32604/cmc.2023.044948 - 26 December 2023

Abstract The original whale optimization algorithm (WOA) has a low initial population quality and tends to converge to local optimal solutions. To address these challenges, this paper introduces an improved whale optimization algorithm called OLCHWOA, incorporating a chaos mechanism and an opposition-based learning strategy. This algorithm introduces chaotic initialization and opposition-based initialization operators during the population initialization phase, thereby enhancing the quality of the initial whale population. Additionally, including an elite opposition-based learning operator significantly improves the algorithm’s global search capabilities during iterations. The work and contributions of this paper are primarily reflected in two aspects.… More >

Shanshan Wang^1,2,3, Quan Yuan¹, Weiwei Tan¹, Tengfei Yang¹, Liang Zeng^1,2,3,*

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3057-3075, 2023, DOI:10.32604/cmc.2023.044807 - 26 December 2023

Abstract Feature Selection (FS) is an important problem that involves selecting the most informative subset of features from a dataset to improve classification accuracy. However, due to the high dimensionality and complexity of the dataset, most optimization algorithms for feature selection suffer from a balance issue during the search process. Therefore, the present paper proposes a hybrid Sine-Cosine Chimp Optimization Algorithm (SCChOA) to address the feature selection problem. In this approach, firstly, a multi-cycle iterative strategy is designed to better combine the Sine-Cosine Algorithm (SCA) and the Chimp Optimization Algorithm (ChOA), enabling a more effective search… More >

Kanhua Su, Jian Wei^*, Meng Li, Hao Li, Wenghao Da, Lang Zhang

Sound & Vibration, Vol.57, pp. 97-117, 2023, DOI:10.32604/sv.2023.043734 - 14 December 2023

Abstract During the drilling process, stick-slip vibration of the drill string is mainly caused by the nonlinear friction generated by the contact between the drill bit and the rock. To eliminate the fatigue wear of downhole drilling tools caused by stick-slip vibrations, the Fractional-Order Proportional-Integral-Derivative (FOPID) controller is used to suppress stick-slip vibrations in the drill string. Although the FOPID controller can effectively suppress the drill string stick-slip vibration, its structure is flexible and parameter setting is complicated, so it needs to use the corresponding machine learning algorithm for parameter optimization. Based on the principle of… More > Graphic Abstract

Dazhi Wang^*, Pengyi Pan, Bowen Niu

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1535-1555, 2023, DOI:10.32604/cmc.2023.042286 - 29 November 2023

Abstract The permanent magnet eddy current coupler (PMEC) solves the problem of flexible connection and speed regulation between the motor and the load and is widely used in electrical transmission systems. It provides torque to the load and generates heat and losses, reducing its energy transfer efficiency. This issue has become an obstacle for PMEC to develop toward a higher power. This paper aims to improve the overall performance of PMEC through multi-objective optimization methods. Firstly, a PMEC modeling method based on the Levenberg-Marquardt back propagation (LMBP) neural network is proposed, aiming at the characteristics of… More >

Junqiang Jiang^1,2, Zhifang Sun¹, Xiong Jiang¹, Shengjie Jin¹, Yinli Jiang³, Bo Fan^1,*

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1617-1644, 2023, DOI:10.32604/cmc.2023.041973 - 29 November 2023

Abstract The grey wolf optimizer (GWO) is a swarm-based intelligence optimization algorithm by simulating the steps of searching, encircling, and attacking prey in the process of wolf hunting. Along with its advantages of simple principle and few parameters setting, GWO bears drawbacks such as low solution accuracy and slow convergence speed. A few recent advanced GWOs are proposed to try to overcome these disadvantages. However, they are either difficult to apply to large-scale problems due to high time complexity or easily lead to early convergence. To solve the abovementioned issues, a high-accuracy variable grey wolf optimizer… More >

Luoyin Feng^1,*, Jize Du², Chong Fu¹

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1645-1662, 2023, DOI:10.32604/cmc.2023.042630 - 29 November 2023

Abstract In the era of network communication, digital image encryption (DIE) technology is critical to ensure the security of image data. However, there has been limited research on combining deep learning neural networks with chaotic mapping for the encryption of digital images. So, this paper addresses this gap by studying the generation of pseudo-random sequences (PRS) chaotic signals using dual logistic chaotic maps. These signals are then predicted using long and short-term memory (LSTM) networks, resulting in the reconstruction of a new chaotic signal. During the research process, it was discovered that there are numerous training… More >

Abdulaziz Attaallah¹, Khalil al-Sulbi², Areej Alasiry³, Mehrez Marzougui³, Mohd Waris Khan^4,*, Mohd Faizan⁴, Alka Agrawal⁵, Dhirendra Pandey⁵

Computer Systems Science and Engineering, Vol.47, No.3, pp. 3165-3195, 2023, DOI:10.32604/csse.2023.040259 - 09 November 2023

Abstract Security testing is a critical concern for organizations worldwide due to the potential financial setbacks and damage to reputation caused by insecure software systems. One of the challenges in software security testing is test case prioritization, which aims to reduce redundancy in fault occurrences when executing test suites. By effectively applying test case prioritization, both the time and cost required for developing secure software can be reduced. This paper proposes a test case prioritization technique based on the Ant Colony Optimization (ACO) algorithm, a metaheuristic approach. The performance of the ACO-based technique is evaluated using More >