Mohamad Khairulamirin Md Razali^1,*, Masri Ayob², Abdul Hadi Abd Rahman², Razman Jarmin³, Chian Yong Liu³, Muhammad Maaya³, Azarinah Izaham³, Graham Kendall^4,5

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.2, pp. 1233-1288, 2025, DOI:10.32604/cmes.2025.060481 - 27 January 2025

Abstract The Cross-domain Heuristic Search Challenge (CHeSC) is a competition focused on creating efficient search algorithms adaptable to diverse problem domains. Selection hyper-heuristics are a class of algorithms that dynamically choose heuristics during the search process. Numerous selection hyper-heuristics have different implementation strategies. However, comparisons between them are lacking in the literature, and previous works have not highlighted the beneficial and detrimental implementation methods of different components. The question is how to effectively employ them to produce an efficient search heuristic. Furthermore, the algorithms that competed in the inaugural CHeSC have not been collectively reviewed. This… More >

Jiangchuan Li, Peisong He^*, Jiayong Liu, Jie Luo, Qiang Xia

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 4893-4915, 2023, DOI:10.32604/cmc.2023.036819 - 29 April 2023

Abstract Steganography techniques, such as audio steganography, have been widely used in covert communication. However, the deep neural network, especially the convolutional neural network (CNN), has greatly threatened the security of audio steganography. Besides, existing adversarial attacks-based countermeasures cannot provide general perturbation, and the transferability against unknown steganography detection methods is weak. This paper proposes a cover enhancement method for audio steganography based on universal adversarial perturbations with sample diversification to address these issues. Universal adversarial perturbation is constructed by iteratively optimizing adversarial perturbation, which applies adversarial attack techniques, such as Deepfool. Moreover, the sample diversification… More >

Minakshi Kalra¹, Vijay Kumar², Manjit Kaur³, Sahar Ahmed Idris⁴, Şaban Öztürk⁵, Hammam Alshazly^6,*

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 6239-6255, 2022, DOI:10.32604/cmc.2022.020682 - 11 October 2021

Abstract Nowadays, due to the increase in information resources, the number of parameters and complexity of feature vectors increases. Optimization methods offer more practical solutions instead of exact solutions for the solution of this problem. The Emperor Penguin Optimizer (EPO) is one of the highest performing meta-heuristic algorithms of recent times that imposed the gathering behavior of emperor penguins. It shows the superiority of its performance over a wide range of optimization problems thanks to its equal chance to each penguin and its fast convergence features. Although traditional EPO overcomes the optimization problems in continuous search… More >