Mohammed A. El-Shorbagy¹, Anas Bouaouda^2,*, Laith Abualigah^3,4, Fatma A. Hashim^5,6

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2339-2404, 2025, DOI:10.32604/cmes.2025.061028 - 03 March 2025

Abstract With the rapid advancements in technology and science, optimization theory and algorithms have become increasingly important. A wide range of real-world problems is classified as optimization challenges, and meta-heuristic algorithms have shown remarkable effectiveness in solving these challenges across diverse domains, such as machine learning, process control, and engineering design, showcasing their capability to address complex optimization problems. The Stochastic Fractal Search (SFS) algorithm is one of the most popular meta-heuristic optimization methods inspired by the fractal growth patterns of natural materials. Since its introduction by Hamid Salimi in 2015, SFS has garnered significant attention… More >

Weiqiang Jin^1,#, Xingwu Tian^1,#, Bohang Shi¹, Biao Zhao^1,*, Haibin Duan², Hao Wu³

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3523-3553, 2024, DOI:10.32604/cmc.2024.055125 - 12 September 2024

Abstract The UAV pursuit-evasion problem focuses on the efficient tracking and capture of evading targets using unmanned aerial vehicles (UAVs), which is pivotal in public safety applications, particularly in scenarios involving intrusion monitoring and interception. To address the challenges of data acquisition, real-world deployment, and the limited intelligence of existing algorithms in UAV pursuit-evasion tasks, we propose an innovative swarm intelligence-based UAV pursuit-evasion control framework, namely “Boids Model-based DRL Approach for Pursuit and Escape” (Boids-PE), which synergizes the strengths of swarm intelligence from bio-inspired algorithms and deep reinforcement learning (DRL). The Boids model, which simulates collective… More >

Punam Gulande^*, R. N. Awale

Computer Systems Science and Engineering, Vol.48, No.4, pp. 937-952, 2024, DOI:10.32604/csse.2024.046123 - 17 July 2024

Abstract The study of gene expression has emerged as a vital tool for cancer diagnosis and prognosis, particularly with the advent of microarray technology that enables the measurement of thousands of genes in a single sample. While this wealth of data offers invaluable insights for disease management, the high dimensionality poses a challenge for multiclass classification. In this context, selecting relevant features becomes essential to enhance classification model performance. Swarm Intelligence algorithms have proven effective in addressing this challenge, owing to their ability to navigate intricate, non-linear feature-class relationships. This paper introduces a novel hybrid swarm More >

Chen Zhang¹, Liming Liu¹, Yufei Yang¹, Yu Sun¹, Jiaxu Ning², Yu Zhang³, Changsheng Zhang^1,4,*, Ying Guo⁴

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5201-5223, 2024, DOI:10.32604/cmc.2024.050863 - 20 June 2024

Abstract The flying foxes optimization (FFO) algorithm, as a newly introduced metaheuristic algorithm, is inspired by the survival tactics of flying foxes in heat wave environments. FFO preferentially selects the best-performing individuals. This tendency will cause the newly generated solution to remain closely tied to the candidate optimal in the search area. To address this issue, the paper introduces an opposition-based learning-based search mechanism for FFO algorithm (IFFO). Firstly, this paper introduces niching techniques to improve the survival list method, which not only focuses on the adaptability of individuals but also considers the population’s crowding degree More >

Abidhan Bardhan^1,*, Raushan Kumar Singh², Mohammed Alatiyyah³, Sulaiman Abdullah Alateyah^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1521-1555, 2024, DOI:10.32604/cmes.2023.044467 - 29 January 2024

Abstract This research proposes a highly effective soft computing paradigm for estimating the compressive strength (CS) of metakaolin-contained cemented materials. The proposed approach is a combination of an enhanced grey wolf optimizer (EGWO) and an extreme learning machine (ELM). EGWO is an augmented form of the classic grey wolf optimizer (GWO). Compared to standard GWO, EGWO has a better hunting mechanism and produces an optimal performance. The EGWO was used to optimize the ELM structure and a hybrid model, ELM-EGWO, was built. To train and validate the proposed ELM-EGWO model, a sum of 361 experimental results… More >

Hao Li, Jun Tang^*, Qingtao Pan, Jianjun Zhan, Songyang Lao

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2835-2859, 2023, DOI:10.32604/cmc.2023.038670 - 08 October 2023

Abstract No optimization algorithm can obtain satisfactory results in all optimization tasks. Thus, it is an effective way to deal with the problem by an ensemble of multiple algorithms. This paper proposes an ensemble of population-based metaheuristics (EPM) to solve single-objective optimization problems. The design of the EPM framework includes three stages: the initial stage, the update stage, and the final stage. The framework applies the transformation of the real and virtual population to balance the problem of exploration and exploitation at the population level and uses an elite strategy to communicate among virtual populations. The… More >

Zhenyu Yan^1,*, Haotian Bian²

Computer Systems Science and Engineering, Vol.47, No.2, pp. 2241-2257, 2023, DOI:10.32604/csse.2023.040603 - 28 July 2023

Abstract As millimeter waves will be widely used in the Internet of Things (IoT) and Telematics to provide high bandwidth communication and mass connectivity, the coverage optimization of base stations can effectively improve the quality of communication services. How to optimize the convergence speed of the base station coverage solution is crucial for IoT service providers. This paper proposes the Muti-Fusion Sparrow Search Algorithm (MFSSA) optimize the situation to address the problem of discrete coverage maximization and rapid convergence. Firstly, the initial swarm diversity is enriched using a sine chaotic map, and dynamic adaptive weighting is… More >

Siling Feng¹, Yinjie Chen¹, Mengxing Huang^1,2,*, Feng Shu¹

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4341-4355, 2023, DOI:10.32604/cmc.2023.037154 - 31 March 2023

Abstract Due to their adaptability, Unmanned Aerial Vehicles (UAVs) play an essential role in the Internet of Things (IoT). Using wireless power transfer (WPT) techniques, an UAV can be supplied with energy while in flight, thereby extending the lifetime of this energy-constrained device. This paper investigates the optimization of resource allocation in light of the fact that power transfer and data transmission cannot be performed simultaneously. In this paper, we propose an optimization strategy for the resource allocation of UAVs in sensor communication networks. It is a practical solution to the problem of marine sensor networks… More >

Abdelazim G. Hussien^1,2, Guoxi Liang³, Huiling Chen^4,*, Haiping Lin^5,*

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 2267-2289, 2023, DOI:10.32604/cmes.2023.024247 - 09 March 2023

Abstract Many complex optimization problems in the real world can easily fall into local optimality and fail to find the optimal solution, so more new techniques and methods are needed to solve such challenges. Metaheuristic algorithms have received a lot of attention in recent years because of their efficient performance and simple structure. Sine Cosine Algorithm (SCA) is a recent Metaheuristic algorithm that is based on two trigonometric functions Sine & Cosine. However, like all other metaheuristic algorithms, SCA has a slow convergence and may fail in sub-optimal regions. In this study, an enhanced version of More >

Ahmed Najat Ahmed¹, JinHyung Kim², Yunyoung Nam^3,*, Mohamed Abouhawwash^4,5

Computer Systems Science and Engineering, Vol.46, No.2, pp. 2527-2542, 2023, DOI:10.32604/csse.2023.036106 - 09 February 2023

Abstract In this current century, most industries are moving towards automation, where human intervention is dramatically reduced. This revolution leads to industrial revolution 4.0, which uses the Internet of Things (IoT) and wireless sensor networks (WSN). With its associated applications, this IoT device is used to compute the received WSN data from devices and transfer it to remote locations for assistance. In general, WSNs, the gateways are a long distance from the base station (BS) and are communicated through the gateways nearer to the BS. At the gateway, which is closer to the BS, energy drains… More >