Ambika Aggarwal¹, Sunil Kumar^2,3, Ashok Bhansali⁴, Deema Mohammed Alsekait^5,*, Diaa Salama AbdElminaam^6,7,8

Computer Systems Science and Engineering, Vol.48, No.4, pp. 953-967, 2024, DOI:10.32604/csse.2024.050406 - 17 July 2024

Abstract Existing multiple workflow scheduling techniques focus on traditional Quality of Service (QoS) parameters such as cost, deadline, and makespan to find optimal solutions by consuming a large amount of electrical energy. Higher energy consumption decreases system efficiency, increases operational cost, and generates more carbon footprint. These major problems can lead to several problems, such as economic strain, environmental degradation, resource depletion, energy dependence, health impacts, etc. In a cloud computing environment, scheduling multiple workflows is critical in developing a strategy for energy optimization, which is an NP-hard problem. This paper proposes a novel, bi-phase Energy-Efficient… More >

Sharaf Alzoubi¹, Laith Abualigah^{2,3,4,5,6,7,8,*}, Mohamed Sharaf⁹, Mohammad Sh. Daoud¹⁰, Nima Khodadadi¹¹, Heming Jia¹²

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 2557-2604, 2024, DOI:10.32604/cmes.2023.045170 - 11 March 2024

Abstract This research paper presents a novel optimization method called the Synergistic Swarm Optimization Algorithm (SSOA). The SSOA combines the principles of swarm intelligence and synergistic cooperation to search for optimal solutions efficiently. A synergistic cooperation mechanism is employed, where particles exchange information and learn from each other to improve their search behaviors. This cooperation enhances the exploitation of promising regions in the search space while maintaining exploration capabilities. Furthermore, adaptive mechanisms, such as dynamic parameter adjustment and diversification strategies, are incorporated to balance exploration and exploitation. By leveraging the collaborative nature of swarm intelligence and More >

Seung-Yeon Hwang¹, Jeong-Joon Kim^2,*

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 4669-4680, 2023, DOI:10.32604/cmc.2023.031129 - 29 April 2023

Abstract With the development of artificial intelligence-related technologies such as deep learning, various organizations, including the government, are making various efforts to generate and manage big data for use in artificial intelligence. However, it is difficult to acquire big data due to various social problems and restrictions such as personal information leakage. There are many problems in introducing technology in fields that do not have enough training data necessary to apply deep learning technology. Therefore, this study proposes a mixed contour data augmentation technique, which is a data augmentation technique using contour images, to solve a… More >

Seung-Yeon Hwang¹, Jeong-Joon Kim^2,*

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3553-3569, 2023, DOI:10.32604/cmc.2023.037028 - 31 March 2023

Abstract Recently, deep learning has achieved remarkable results in fields that require human cognitive ability, learning ability, and reasoning ability. Activation functions are very important because they provide the ability of artificial neural networks to learn complex patterns through nonlinearity. Various activation functions are being studied to solve problems such as vanishing gradients and dying nodes that may occur in the deep learning process. However, it takes a lot of time and effort for researchers to use the existing activation function in their research. Therefore, in this paper, we propose a universal activation function (UA) so… More >

Hanen Yaakoubi¹, Hegazy Rezk², Mujahed Al-Dhaifallah^3,4,*, Joseph Haggège¹

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3657-3675, 2023, DOI:10.32604/cmc.2023.034929 - 31 March 2023

Abstract The present paper aims at validating a Model Predictive Control (MPC), based on the Mixed Logical Dynamical (MLD) model, for Hybrid Dynamic Systems (HDSs) that explicitly involve continuous dynamics and discrete events. The proposed benchmark system is a three-tank process, which is a typical case study of HDSs. The MLD-MPC controller is applied to the level control of the considered tank system. The study is initially focused on the MLD approach that allows consideration of the interacting continuous dynamics with discrete events and includes the operating constraints. This feature of MLD modeling is very advantageous… More >

Wenchuan Wang^1,*, Weican Tian¹, Kwok-wing Chau², Yiming Xue¹, Lei Xu³, Hongfei Zang¹

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.2, pp. 1603-1642, 2023, DOI:10.32604/cmes.2023.026231 - 06 February 2023

Abstract The Bald Eagle Search algorithm (BES) is an emerging meta-heuristic algorithm. The algorithm simulates the hunting behavior of eagles, and obtains an optimal solution through three stages, namely selection stage, search stage and swooping stage. However, BES tends to drop-in local optimization and the maximum value of search space needs to be improved. To fill this research gap, we propose an improved bald eagle algorithm (CABES) that integrates Cauchy mutation and adaptive optimization to improve the performance of BES from local optima. Firstly, CABES introduces the Cauchy mutation strategy to adjust the step size of… More >

Abeer Bashab¹, Ashraf Osman Ibrahim^2,*, Ibrahim Abakar Tarigo Hashem³, Karan Aggarwal⁴, Fadhil Mukhlif⁵, Fuad A. Ghaleb⁵, Abdelzahir Abdelmaboud⁶

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6461-6484, 2023, DOI:10.32604/cmc.2023.034051 - 28 December 2022

Abstract University timetabling problems are a yearly challenging task and are faced repeatedly each semester. The problems are considered non-polynomial time (NP) and combinatorial optimization problems (COP), which means that they can be solved through optimization algorithms to produce the aspired optimal timetable. Several techniques have been used to solve university timetabling problems, and most of them use optimization techniques. This paper provides a comprehensive review of the most recent studies dealing with concepts, methodologies, optimization, benchmarks, and open issues of university timetabling problems. The comprehensive review starts by presenting the essence of university timetabling as… More >

Allam Abumwais^*, Mahmoud Obaid

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 4951-4963, 2023, DOI:10.32604/cmc.2023.032822 - 28 December 2022

Abstract Modern shared-memory multi-core processors typically have shared Level 2 (L2) or Level 3 (L3) caches. Cache bottlenecks and replacement strategies are the main problems of such architectures, where multiple cores try to access the shared cache simultaneously. The main problem in improving memory performance is the shared cache architecture and cache replacement. This paper documents the implementation of a Dual-Port Content Addressable Memory (DPCAM) and a modified Near-Far Access Replacement Algorithm (NFRA), which was previously proposed as a shared L2 cache layer in a multi-core processor. Standard Performance Evaluation Corporation (SPEC) Central Processing Unit (CPU)… More >

Doaa Sami Khafaga¹, El-Sayed M. El-kenawy^2,3, Faten Khalid Karim^1,*, Mostafa Abotaleb⁴, Abdelhameed Ibrahim⁵, Abdelaziz A. Abdelhamid^6,7, D. L. Elsheweikh⁸

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4531-4545, 2023, DOI:10.32604/cmc.2023.033042 - 31 October 2022

Abstract Selecting the most relevant subset of features from a dataset is a vital step in data mining and machine learning. Each feature in a dataset has 2ⁿ possible subsets, making it challenging to select the optimum collection of features using typical methods. As a result, a new metaheuristics-based feature selection method based on the dipper-throated and grey-wolf optimization (DTO-GW) algorithms has been developed in this research. Instability can result when the selection of features is subject to metaheuristics, which can lead to a wide range of results. Thus, we adopted hybrid optimization in our method of… More >

Doaa Sami Khafaga¹, El-Sayed M. El-kenawy², Fadwa Alrowais^1,*, Sunil Kumar³, Abdelhameed Ibrahim⁴, Abdelaziz A. Abdelhamid^5,6

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4027-4041, 2023, DOI:10.32604/cmc.2023.033039 - 31 October 2022

Abstract In data mining and machine learning, feature selection is a critical part of the process of selecting the optimal subset of features based on the target data. There are 2ⁿ potential feature subsets for every n features in a dataset, making it difficult to pick the best set of features using standard approaches. Consequently, in this research, a new metaheuristics-based feature selection technique based on an adaptive squirrel search optimization algorithm (ASSOA) has been proposed. When using metaheuristics to pick features, it is common for the selection of features to vary across runs, which can lead… More >