Jun Wang^1,2, Linxi Zhang^1,2, Hao Zhang¹, Funan Peng^1,*, Mohammed A. El-Meligy³, Mohamed Sharaf³, Qiang Fu¹

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1281-1299, 2024, DOI:10.32604/cmc.2024.048495

Abstract The existing algorithms for solving multi-objective optimization problems fall into three main categories: Decomposition-based, dominance-based, and indicator-based. Traditional multi-objective optimization problems mainly focus on objectives, treating decision variables as a total variable to solve the problem without considering the critical role of decision variables in objective optimization. As seen, a variety of decision variable grouping algorithms have been proposed. However, these algorithms are relatively broad for the changes of most decision variables in the evolution process and are time-consuming in the process of finding the Pareto frontier. To solve these problems, a multi-objective optimization algorithm for grouping decision variables based… More >

Liang Chen¹, Jingbo Zhang¹, Linjie Wu¹, Xingjuan Cai^1,2,*, Yubin Xu¹

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 363-383, 2024, DOI:10.32604/cmes.2024.049044

Abstract The large-scale multi-objective optimization algorithm (LSMOA), based on the grouping of decision variables, is an advanced method for handling high-dimensional decision variables. However, in practical problems, the interaction among decision variables is intricate, leading to large group sizes and suboptimal optimization effects; hence a large-scale multi-objective optimization algorithm based on weighted overlapping grouping of decision variables (MOEAWOD) is proposed in this paper. Initially, the decision variables are perturbed and categorized into convergence and diversity variables; subsequently, the convergence variables are subdivided into groups based on the interactions among different decision variables. If the size of a group surpasses the set… More >

Dan Zhang¹, Yiwen Liang^1,*, Hongbin Dong²

CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.3, pp. 2025-2045, 2023, DOI:10.32604/cmes.2023.022864

Abstract The artificial immune system, an excellent prototype for developing Machine Learning, is inspired by the function of the powerful natural immune system. As one of the prevalent classifiers, the Dendritic Cell Algorithm (DCA) has been widely used to solve binary problems in the real world. The classification of DCA depends on a data pre-processing procedure to generate input signals, where feature selection and signal categorization are the main work. However, the results of these studies also show that the signal generation of DCA is relatively weak, and all of them utilized a filter strategy to remove unimportant attributes. Ignoring filtered… More > Graphic Abstract

J. Shanthini¹, P. Punitha^2,*, S. Karthik²

Computer Systems Science and Engineering, Vol.44, No.3, pp. 2619-2636, 2023, DOI:10.32604/csse.2023.027330

Abstract In today's Internet routing infrastructure, designers have addressed scaling concerns in routing constrained multiobjective optimization problems examining latency and mobility concerns as a secondary constrain. In tactical Mobile Ad-hoc Network (MANET), hubs can function based on the work plan in various social affairs and the internally connected hubs are almost having the related moving standards where the topology between one and the other are tightly coupled in steady support by considering the touchstone of hubs such as a self-sorted out, self-mending and self-administration. Clustering in the routing process is one of the key aspects to increase MANET performance by coordinating… More >

Woo Hyun Park¹, Isma Farah Siddiqui³, Nawab Muhammad Faseeh Qureshi^2,*

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5609-5624, 2022, DOI:10.32604/cmc.2022.030235

Abstract With the advent of the big data era, security issues in the context of artificial intelligence (AI) and data analysis are attracting research attention. In the metaverse, which will become a virtual asset in the future, users’ communication, movement with characters, text elements, etc., are required to integrate the real and virtual. However, they can be exposed to threats. Particularly, various hacker threats exist. For example, users’ assets are exposed through notices and mail alerts regularly sent to users by operators. In the future, hacker threats will increase mainly due to naturally anonymous texts. Therefore, it is necessary to use… More >

R. S. Latha^1,*, B. Saravana Balaji², Nebojsa Bacanin³, Ivana Strumberger³, Miodrag Zivkovic³, Milos Kabiljo³

Computer Systems Science and Engineering, Vol.43, No.1, pp. 317-332, 2022, DOI:10.32604/csse.2022.020487

Abstract Big data are regarded as a tremendous technology for processing a huge variety of data in a short time and with a large storage capacity. The user’s access over the internet creates massive data processing over the internet. Big data require an intelligent feature selection model by addressing huge varieties of data. Traditional feature selection techniques are only applicable to simple data mining. Intelligent techniques are needed in big data processing and machine learning for an efficient classification. Major feature selection algorithms read the input features as they are. Then, the features are preprocessed and classified. Here, an algorithm does… More >

Pagnchakneat C. Ouk¹, Wooguil Pak^2,*

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 381-398, 2022, DOI:10.32604/cmc.2022.024540

Abstract Although Android becomes a leading operating system in market, Android users suffer from security threats due to malwares. To protect users from the threats, the solutions to detect and identify the malware variant are essential. However, modern malware evades existing solutions by applying code obfuscation and native code. To resolve this problem, we introduce an ensemble-based malware classification algorithm using malware family grouping. The proposed family grouping algorithm finds the optimal combination of families belonging to the same group while the total number of families is fixed to the optimal total number. It also adopts unified feature extraction technique for… More >

G. Niranjani^1,*, K. Umamaheswari²

Intelligent Automation & Soft Computing, Vol.33, No.1, pp. 457-472, 2022, DOI:10.32604/iasc.2022.024264

Abstract COVID-19 pandemic has imposed many threats. One among them is the accumulation of waste in hospitals. Waste should be disposed regularly and safely using sustainable methods. Sustainability is self development with preservation of society and its resources. The main objective of this research is to achieve sustainability in waste collection by minimizing the cost factor. Minimization of sustainable-cost involves minimization of three sub-components – total travel-cost representing economical component, total emission-cost representing environmental component and total driver-allowance-cost representing social component. Most papers under waste collection implement Tabu search algorithm and fail to consider the environmental and social aspects involved. We… More >

Luciano Pereira da Silva^1,*, Bruno Benato Rutyna¹, Aline Roberta Santos Righi², Marcio Augusto Villela Pinto³

CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.2, pp. 699-715, 2021, DOI:10.32604/cmes.2021.014239

Abstract In this article, we improve the order of precision of the two-dimensional Poisson equation by combining extrapolation techniques with high order schemes. The high order solutions obtained traditionally generate non-sparse matrices and the calculation time is very high. We can obtain sparse matrices by applying compact schemes. In this article, we compare compact and exponential finite difference schemes of fourth order. The numerical solutions are calculated in quadruple precision (Real * 16 or extended precision) in FORTRAN language, and iteratively obtained until reaching the round-off error magnitude around 1.0E −32. This procedure is performed to ensure that there is no… More >

Jianhui Wu^1,2, Feng Huang^1,2, Wenjing Hu²,Wei He^1,2, Bing Tu^1,2, Longyuan Guo^1,2, Xianfeng Ou^1,2,*

Computer Systems Science and Engineering, Vol.33, No.5, pp. 327-334, 2018, DOI:10.32604/csse.2018.33.327

Abstract Face detection is very important in video surveillance of public safety. This paper proposed a face detection method based on the best optimization convex grouping to detect the face regions from different face shape images at actual conditions. Firstly, the basic principle of convex grouping was discussed, the main rules of convex and the structure of the convex polygons was described. And then the best optimization convex grouping algorithm of the convex polygons was designed. At last, all of the algorithms, which used the best optimization convex grouping to detect the face region on the data set of MIT single… More >