Yuqian Qi^*, Yanbo Che, Liangliang Liu, Jiayu Ni, Shangyuan Zhang

Energy Engineering, Vol.122, No.10, pp. 3999-4017, 2025, DOI:10.32604/ee.2025.068054 - 30 September 2025

Abstract The process of including renewable energy sources in power networks is moving quickly, so the need for innovative configuration solutions for grid-side ESS has grown. Among the new methods presented in this paper is GA-OCESE, which stands for Genetic Algorithm-based Optimization Configuration for Energy Storage in Electric Networks. This is one of the methods suggested in this study, which aims to enhance the sizing, positioning, and operational characteristics of structured ESS under dynamic grid conditions. Particularly, the aim is to maximize efficiency. A multiobjective genetic algorithm, the GA-OCESE framework, considers all these factors simultaneously. Besides… More >

KL Vasudev¹, Manish Pandey², Jaan H. Pu^3,*

FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.7, pp. 1545-1569, 2025, DOI:10.32604/fdmp.2025.065289 - 31 July 2025

Abstract This study presents a comparative analysis of optimisation strategies for designing hull shapes of Autonomous Underwater Vehicles (AUVs), paying special attention to drag, lift-to-drag ratio, and delivered power. A fully integrated optimisation framework is developed accordingly, combining a single-objective Genetic Algorithm (GA) for design parameter generation, Computer-Aided Geometric Design (CAGD) for the creation of hull geometries and associated fluid domains, and a Reynolds-Averaged Navier–Stokes (RANS) solver for evaluating hydrodynamic performance metrics. This unified approach eliminates manual intervention, enabling automated determination of optimal hull configurations. Three distinct optimisation problems are addressed using the proposed methodology. First,… More >

Iman S. Al-Mahdi¹, Saad M. Darwish^1,*, Magda M. Madbouly²

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 875-909, 2025, DOI:10.32604/cmes.2025.061623 - 11 April 2025

Abstract Heart disease prediction is a critical issue in healthcare, where accurate early diagnosis can save lives and reduce healthcare costs. The problem is inherently complex due to the high dimensionality of medical data, irrelevant or redundant features, and the variability in risk factors such as age, lifestyle, and medical history. These challenges often lead to inefficient and less accurate models. Traditional prediction methodologies face limitations in effectively handling large feature sets and optimizing classification performance, which can result in overfitting poor generalization, and high computational cost. This work proposes a novel classification model for heart… More >

Shimaa M. Amer¹, Ashraf A. M. Khalaf², Amr H. Hussein^3,4, Salman A. Alqahtani⁵, Mostafa H. Dahshan⁶, Hossam M. Kassem^3,4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2749-2767, 2024, DOI:10.32604/cmes.2023.029138 - 15 December 2023

Abstract Side lobe level reduction (SLL) of antenna arrays significantly enhances the signal-to-interference ratio and improves the quality of service (QOS) in recent and future wireless communication systems starting from 5G up to 7G. Furthermore, it improves the array gain and directivity, increasing the detection range and angular resolution of radar systems. This study proposes two highly efficient SLL reduction techniques. These techniques are based on the hybridization between either the single convolution or the double convolution algorithms and the genetic algorithm (GA) to develop the Conv/GA and DConv/GA, respectively. The convolution process determines the element’s… More >

Amel Austine^*, R. Suji Pramila

Computer Systems Science and Engineering, Vol.46, No.2, pp. 2263-2276, 2023, DOI:10.32604/csse.2023.032323 - 09 February 2023

Abstract In a cellular network, direct Device-to-Device (D2D) communication enhances Quality of Service (QoS) in terms of coverage, throughput and amount of power consumed. Since the D2D pairs involve cellular resources for communication, the chances of interference are high. D2D communications demand minimum interference along with maximum throughput and sum rate which can be achieved by employing optimal resources and efficient power allocation procedures. In this research, a hybrid optimization model called Genetic Algorithm-Adaptive Bat Optimization (GA-ABO) algorithm is proposed for efficient resource allocation in a cellular network with D2D communication. Simulation analysis demonstrates that the More >

Mohit Agarwal^1,*, Shikha Gupta²

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 6103-6119, 2022, DOI:10.32604/cmc.2022.030778 - 28 July 2022

Abstract Task scheduling in highly elastic and dynamic processing environments such as cloud computing have become the most discussed problem among researchers. Task scheduling algorithms are responsible for the allocation of the tasks among the computing resources for their execution, and an inefficient task scheduling algorithm results in under-or over-utilization of the resources, which in turn leads to degradation of the services. Therefore, in the proposed work, load balancing is considered as an important criterion for task scheduling in a cloud computing environment as it can help in reducing the overhead in the critical decision-oriented process.… More >

Adel Hamdan Mohammad^1,*, Tariq Alwada’n², Omar Almomani³, Sami Smadi³, Nidhal ElOmari⁴

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 133-150, 2022, DOI:10.32604/cmc.2022.027475 - 18 May 2022

Abstract Intrusion detection is a serious and complex problem. Undoubtedly due to a large number of attacks around the world, the concept of intrusion detection has become very important. This research proposes a multilayer bio-inspired feature selection model for intrusion detection using an optimized genetic algorithm. Furthermore, the proposed multilayer model consists of two layers (layers 1 and 2). At layer 1, three algorithms are used for the feature selection. The algorithms used are Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and Firefly Optimization Algorithm (FFA). At the end of layer 1, a priority value… More >

Rahul Dubey^*, Jitendra Agrawal

Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 747-763, 2022, DOI:10.32604/iasc.2022.020975 - 17 November 2021

Abstract Extracting the features from an image is a cumbersome task. Initially, this task was performed by domain experts through a process known as handcrafted feature design. A deep embedding technique known as convolutional neural networks (CNNs) later solved this problem by introducing the feature learning concept, through which the CNN is directly provided with images. This CNN then learns the features of the image, which are subsequently given as input to the further layers for an intended task like classification. CNNs have demonstrated astonishing performance in several practicable applications in the last few years. Nevertheless,… More >

Mohammad Javad Jafari¹, Mohsen Niknam Sharak², Ali Khavanin³, Touraj Ebadzadeh⁴, Mahmood Fazlali⁵, Rohollah Fallah Madvari^6,*

Sound & Vibration, Vol.55, No.2, pp. 117-130, 2021, DOI:10.32604/sv.2021.09729 - 21 April 2021

Abstract Fabricating of metal foams with desired morphological parameters including pore size, porosity and pore opening is possible now using sintering technology. Thus, if it is possible to determine the morphology of metal foam to absorb sound at a given frequency, and then fabricate it through sintering, it is expected to have optimized metal foams for the best sound absorption. Theoretical sound absorption models such as Lu model describe the relationship between morphological parameters and the sound absorption coefficient. In this study, the Lu model was used to optimize the morphological parameters of Aluminum metal foam… More >

Abayomi-Alli A.¹, Misra S.^2,3, Fernández-Sanz L.⁴, Abayomi-Alli O.^2,*, Edun A. R.¹

Intelligent Automation & Soft Computing, Vol.26, No.3, pp. 385-396, 2020, DOI:10.32604/iasc.2020.013915

Abstract University timetable scheduling is a complicated constraint problem because educational institutions use timetables to maximize and optimize scarce resources, such as time and space. In this paper, an examination timetable system using Genetic Algorithm and Tabu Search memory with course sandwiching (GAT_CS), was developed for a large public University. The concept of Genetic Algorithm with Selection and Evaluation was implemented while the memory properties of Tabu Search and course sandwiching replaced Crossover and Mutation. The result showed that GAT_CS had hall allocation accuracies of 96.07% and 99.02%, unallocated score of 3.93% and 0.98% for first More >