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Intelligent Systems and Computational Networks (ICISCN), International Conference on

作者： Qi Zhang Sichuan Vocational College of Culture and Communication Chengdu China

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

With the swift progression of intelligent automation, its applications in architecture and interior design have significantly evolved, leading to more efficient and intelligent solutions for floor plan layout generation. Traditional methods for indoor floor planning rely on heuristic or rule-based approaches, which often fail to optimize space utilization, accessibility, and functional requirements effectively. Existing AI-powered approaches, including convolutional neural networks (CNNs) and deep reinforcement learning, have shown significant potential but face challenges in capturing long-term dependencies and dynamic adjustments in complex layouts. To overcome these challenges, we propose a novel approach that leverages Gated Recurrent Units (GRU) to model spatial relationships and sequential dependencies in floor plan generation. Additionally, we introduce the whale optimization Algorithm (WOA) to enhance the efficiency of layout optimization by improving space utilization, room positioning, and traffic flow. The proposed method is evaluated using a benchmark dataset of architectural floor plans, achieving a layout coherence score of 94.3%, space optimization efficiency of 92.7%, and computational speed improvement of 28.5% compared to existing deep learning-based approaches. The deployment of this system can significantly enhance the automation and intelligence of indoor architectural design, offering a practical and efficient solution for architects and urban planners.

关键词： Intelligent automation Layout Refining Manuals whale optimization algorithms Real-time systems Computational efficiency Planning Floors optimization

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An Enhanced Evolutionary Algorithm for Optimized Real-Time Scheduling in Cloud Computing

An Enhanced Evolutionary Algorithm for Optimized Real-Time S...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Asia Bataineh Hamzah Alqudah Mohammad A. Alsharaiah Waleed Awwad Computer Science Department Faculty of Information Technology University of Petra (UoP) Amman Jordan Department of Data Science and Artificial Intelligence Faculty of Information Technology American University of Madaba (AUM) Amman Jordan Information Technology Department King Abdullah II School for Information Technology The University of Jordan (JU) Amman Jordan Department of Computer Science College of Science Northern Border University (NBU) Arar KSA

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

The exponential growth of cloud computing applications has posed significant challenges in achieving efficient resource management, reduced processing time, high through-put, and minimized energy consumption. Despite advancements, existing evolutionary algorithms often suffer from premature convergence, inefficient exploration-exploitation balance, and high energy overhead, particularly under heavy workloads. This paper proposes an enhanced hybrid whale optimization Algorithm (WOA) integrated with the Lévy Flight Mechanism for optimized resource management in cloud computing environments to address these challenges. The primary motivation for this work stems from the limitations observed in state-of-the-art algorithms. The research aims to bridge this gap by designing an evolutionary-based approach that improves resource utilization, reduces task execution delays, and optimizes energy consumption under varying workloads. The proposed model employs the WOA, enhanced with the Lévy Flight Mechanism, to ensure a more effective balance between exploration and exploitation during optimization. Extensive simulations were conducted on workloads ranging from 100 to 700 tasks, comparing the proposed method against leading algorithms. The results demonstrate that the proposed algorithm consistently outperforms the state-of-the-art techniques. Specifically, it achieves higher resource utilization (1.1 vs. 0.9), lower processing time, improved throughput (up to 500), and reduced energy consumption (50 joules compared to 70+ joules) under heavy workloads. These findings validate the enhanced WOA's effectiveness in addressing critical cloud computing optimization problems. The proposed approach provides a robust solution for dynamic resource allocation, ensuring energy efficiency and performance scalability, making it suitable for modern cloud-based environments with increasing computational demands.

关键词： Cloud computing Energy consumption Processor scheduling Scalability Evolutionary computation Throughput whale optimization algorithms Real-time systems Resource management optimization

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A Multi-Optimizer Metaheuristic Approach for Solving Constrained Patch Scheduling Problem

A Multi-Optimizer Metaheuristic Approach for Solving Constra...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Louay Karadsheh Faten Hamad Hussam Fakhouri Faculty of Information Technology University of Petra Amman Jordan Sultan Qaboos University Sultanate of Oman The University of Jordan Jordan

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Timely security patches are crucial in reducing vulnerabilities across computing infrastructures, yet excessive downtime or violations of concurrency constraints can adversely affect service availability and overall performance. In this work, we optimize the problem of constrained patch scheduling using a multi-optimizer metaheuristic approach. We first model patch scheduling as an optimization problem aiming to minimize both total downtime and vulnerability exposure while also respecting concurrency limits, which restrict the number of machines simultaneously offline. The objective function incorporates a penalty factor to discourage over-concurrent patching. We compare eight modern metaheuristic algorithms with variable patch durations. Extensive experiments are conducted, analyzing best objective scores, downtime, exposure, concurrency penalty, computational time, and convergence trends. **Our findings indicate that Equilibrium Optimizer (EO) achieves the lowest final cost (6.5351), followed closely by Multi-Verse Optimizer (MVO) (6.8295). Whereas Grey Wolf Optimizer (GWO) and Moth Flame optimization (MFO), yield moderate results, while whale optimization Algorithm (WOA) exhibits the highest cost (10.3944). These outcomes underscore the impact of concurrency-limited patching constraints on performance and emphasize the benefits of selecting optimizers that effectively balance exploration and exploitation for scheduling in security-critical environments.

关键词： Concurrent computing Costs Processor scheduling Metaheuristics Fires Market research Linear programming whale optimization algorithms Security Convergence

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Research on Noise Reduction Methods for Wheel Force Sensor Signals

Research on Noise Reduction Methods for Wheel Force Sensor S...

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Sensors and Information Technology (ICSI), International Conference on

作者： Junjie Yu Huibin Cao Qiang Li Yuxiang Sun Hu Xu School of Electronic and Information Engineering Anhui Jianzhu University Hefei China Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei China College of Big Data and Internetline Shenzhen Technology University Shenzhen China

ISBN: (数字)9798331542887

ISBN: (纸本)9798331542894

The wheel force data acquisition system for six-component force measurements often encounters noise signals during the data acquisition process. If these noises are not removed, they can severely impact the accuracy of vehicle performance analysis. This paper proposes a noise reduction method (IWOA-VMD-IWT) based on an improved whale optimization algorithm combined with variational modal decomposition and improved wavelet thresholding. By using IWOA to optimize the VMD parameters, the sensor signal is decomposed into intrinsic mode functions (IMFs). These IMFs are then classified into noise-dominant and signal-dominant components based on similarity results. The noise-dominant components undergo the improved wavelet thresholding (IWT) process, and the noise-reduced signal is obtained through final reconstruction. The experimental results indicate that the SNR exceeds 14dB and the RMSE is below 0.17. This represents an improvement of over 8dB in SNR and a reduction of more than 0.14 in RMSE compared to traditional wavelet threshold noise reduction methods. These findings demonstrate the feasibility of the proposed method in vehicle motion analysis.

关键词： Accuracy Noise reduction Data acquisition Wheels whale optimization algorithms Space exploration Performance analysis Force sensors Root mean square Signal to noise ratio

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An Improved Resource Allocation in 5G C-RAN using Hybrid Swarm Intelligence Algorithm

An Improved Resource Allocation in 5G C-RAN using Hybrid Swa...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Voore Subba Rao G. Ravindra Babu Department of CSE APJ Abdul Kalam Technical University in Lucknow India Department of CSE Swami Vivekanand Subharti University Meerut India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

C-RAN is known as Cloud Radio Access Network is a centralized Radio Access Network is an architecture specialized design for radio access network for co-ordinate 2G, 3G,4G and 5G mobile communications for future wireless communications standards. In C-RAN architecture all BBUs are include in base stations will be connected to a Baseband Unit of a centralized pool.. The frequence known as RD is in-expensive RRH are in C-RAN architecture. The Basseband Units are efficient computational tasks are consuming more energy. It is essential to minimize energy consumption and maximize energy efficiency. The resource allocation is a strategy to satisfy Quality of Services, and balance energy efficiency this paper proposed a hybrid method of dynamic resource allocation management algorithm ITS-WOA(Improved Tunicate Swarm whale optimization) to minimize the energy consumption of Baseband Units and also minimize energy consumption by Remote Radio Access. The optimization method is to minimize usage of RRH mapping with BBU and deployed need for RRH and BBU deployment to minimize the operational cost. There should be a provision of active and sleep mode of selection for the both RRH and BBU to minimize energy. The active state shows BBU and RRH are in running state. And sleep mode state shows BBU and RRH are in sleep mode to minimize the energy consumption and maximize the energy utilization for the maintenance of Quality of Service in C-RAN architecture.

关键词： Energy consumption Costs Baseband Heuristic algorithms Computer architecture Quality of service whale optimization algorithms Energy efficiency Resource management Particle swarm optimization

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Investigations of PAPR reduction methods in FBMC System

Investigations of PAPR reduction methods in FBMC System

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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： Rashmi Rani Samantaray Siddappaji Department of Electronics and Communication VTU HKBK College Of Engineering Bengaluru India Department of Electronics and Communication BMS College of Engineering Bengaluru India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515928

The primary concern of the 5G radio network has sparked interest in the development of new waveforms beyond 4G (5th generation networks). Filter Bank Multicarrier (FBMC) waveforms have recently been suggested as the desired one for 5G networks. It has both advantages and disadvantages. One of the biggest drawback of the FBMC broadcast signal is its high Peak to Average Power Ratio (PAPR). It has been widely used in wireless communication to achieve high speed and data rates. A high PAPR system results in substantial distortion of the signal because of the nonlinear power amplifier. Therefore, methods that effectively reduce PAPR are necessary for the realization of the FBMC system. The FBMC system uses the some common techniques as the OFDM system. Comparative analysis of various approaches such as PTS, SLM, Companding, etc are performed by considering their simulation outcomes when it comes to effective spectrum utilization, improved PAPR, along with minimum complexity.

关键词： Wireless communication 5G mobile communication Nonlinear distortion Metaheuristics Power amplifiers Peak to average power ratio whale optimization algorithms Complexity theory Radio networks Particle swarm optimization

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Designing an Efficient Product Image Classification Framework Using Adaptive YoloV5 with Long Short-Term Memory in E-Commerce Platform

Designing an Efficient Product Image Classification Framewor...

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Cognitive Computing in Engineering, Communications, Sciences and Biomedical Health Informatics (IC3ECSBHI), International Conference on

作者： Munmi Dutta Amrita Ganguly Department of Electrical Engineering Assam Engineering College Guwahati Assam India

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

Presently, the online store supports the customers to buy the desired products in online. So, huge competitions are developed in the Electronic Commerce (E-commerce) sector for developing the technology. Most of the E-commerce platforms represent the particular products that users enquire about and also display other similar objects also to improve the interest among the online customers. As the online platforms hold more variety of products, it is essential to analyze the similar products sold together, and also its recommendation is needed. Sentimental analysis plays a major role in opinion mining, which helps to enhance product recommendations to online users. Every day enormous data are generated in the E-commerce and the data are utilized by online retailers to identify customer expectations. Using enormous resources, sentimental analysis models process multiple online options. Hence, it is important to construct a new product image classification technique for the E-commerce platform by considering the limitations of existing techniques. At first, essential images are acquired from standard sources. The gathered images are provided to the product image classification phase. Here, effective product image classification procedures are executed using a developed framework named Adaptive YoloV5 with Long Short-Term Memory (A-YoloV5-LSTM). Also, the weights and hyper-parameters are tuned using Beluga whale optimization (BWO). Further, the efficiency of the recommended product image classification technique is validated over different performance metrics.

关键词： YOLO Deep learning Accuracy Neurons Feature extraction whale optimization algorithms Electronic commerce Long short term memory Standards Image classification

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Grey Wolf Optimizer and whale optimization Algorithm for Stochastic Inventory Management of Reusable Products in a Two-Level Supply Chain

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IEEE ACCESS 2023年 11卷 40278-40297页

作者： Sadeghi, Amir Hossein Bani, Erfan Amani Fallahi, Ali Handfield, Robert North Carolina State Univ Dept Ind & Syst Engn Raleigh NC 27606 USA Sharif Univ Technol Dept Ind Engn Tehran *** Iran North Carolina State Univ Dept Business Management Raleigh NC 27607 USA

Product reuse and recovery is an efficient tool that helps companies to simultaneously address economic and environmental dimensions of sustainability. This paper presents a novel problem for stock management of reusable products in a single-vendor, multi-product, multi-retailer network. Several constraints, such as the maximum budget, storage capacity, number of orders, etc., are considered in their stochastic form to establish a more realistic problem. The presented problem is formulated using a nonlinear programming mathematical model. The chance-constrained approach is suggested to deal with the constraints' uncertainty. Regarding the nonlinearity of the model, grey wolf optimizer (GWO) and whale optimization algorithm (WOA) as two novel metaheuristics are presented as solution approaches, and the sequential quadratic programming (SQP) exact algorithm validates their performance. The parameters of algorithms are calibrated using the Taguchi method for the design of experiments. Extensive analysis is established by solving several numerical results in different sizes and utilizing several comparison measures. Also, the results are compared statistically using proper parametric and non-parametric tests. The analysis of the results shows a significant difference between the algorithms, and GWO has a better performance for solving the presented problem. In addition, both algorithms perform well in searching the solution space, where the GWO and WOA differences with the optimal solution of the SQP algorithm are negligible.

关键词： Supply chains Inventory management Uncertainty Stochastic processes Metaheuristics whale optimization algorithms Programming Reuse and recovery chance-constrained programming grey wolf optimizer whale optimization algorithm Taguchi method

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Optimized Multi-User Dependent Tasks Offloading in Edge-Cloud Computing Using Refined whale optimization Algorithm

IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING

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IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING 2024年第1期9卷 14-30页

作者： Hosny, Khalid M. Awad, Ahmed I. Khashaba, Marwa M. Fouda, Mostafa M. Guizani, Mohsen Mohamed, Ehab R. Zagazig Univ Fac Comp & Informat Dept Informat Technol Zagazig 44519 Egypt Idaho State Univ Dept Elect & Comp Engn Pocatello ID 83209 USA Mohamed Bin Zayed Univ Artificial Intelligence MBZ Machine Learning Dept Masdar City Abu Dhabi U Arab Emirates Zagazig Univ Dept Informat Technol Zagazig 44519 Egypt

Despite the extensive use of IoT and mobile devices in the different applications, their computing power, memory, and battery life are still limited. Multi-Access Edge Computing (MEC) has recently emerged to address the drawbacks of these limitations. With MEC on the network's edge, mobile and IoT devices can offload their computing operations to adjacent edge servers or remote cloud servers. However, task offloading is still a challenging research issue, and it is necessary to improve the overall Quality of Service (QoS) and attain optimized performance and resource utilization. Another crucial issue that is usually overlooked while handling this matter is offloading an application that consists of dependent tasks. In this study, we suggest a Refined whale optimization Algorithm (RWOA) for solving the multiuser dependent tasks offloading problem in the Edge-Cloud computing environment with three objectives: 1- minimizing the application execution latency, 2- minimizing the energy consumption of end devices, and 3- the charging cost for used resources. We also avoid the traditional binary planning mechanisms by allowing each task to be partially processed simultaneously at three processing locations (local device, MEC, cloud). We compare RWOA with other Optimizers, and the results demonstrate that the RWOA has optimized the fitness by 52.7% relative to the second best comparison optimizer.

关键词： Task analysis Cloud computing Servers Internet of Things Energy consumption Costs whale optimization algorithms multi-access edge computing multi-objective computational offloading multi-user scenario task dependency whale optimization algorithm

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Cloudlet Based Computing optimization Using Variable-Length whale optimization and Differential Evolution

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IEEE ACCESS 2023年 11卷 45098-45112页

作者： Muwafaq, Layth Noordin, Nor K. K. Othman, Mohamed Ismail, Alyani Hashim, Fazirulhisyam Univ Putra Malaysia Fac Engn Dept Comp & Commun Syst Engn Serdang 43400 Selangor D E Malaysia Univ Putra Malaysia Fac Engn Res Ctr Excellence Wireless & Photon Network WiPNE Serdang 43400 Selangor D E Malaysia Univ Putra Malaysia UPM Dept Commun Technol & Networks Serdang 43400 Selangor D E Malaysia Univ Putra Malaysia UPM Inst Math Res INSPEM Lab Computat Sci & Math Phys Serdang 43400 Selangor D E Malaysia

Cloudlet-based optimization involves deploying a set of cloudlets in an environment and assigning user tasks to optimize various metrics, including energy consumption, quality of service (QoS), and cost. Typically, approaches deal with them separately, which might cause sub-optimality. Furthermore, assuming the fixed location of the cloudlets will limit the dynamic adaptability of the problem. Enabling more optimality and adaptability to the dynamic nature of cloudlet-based computing, we propose a novel Variable-Length multi-objective whale optimization Integrated with Differential Evolution designated as VL-WIDE. Unlike the existing optimization algorithm, VL-WIDE features the capability of searching different lengths of solutions to cover the variable number of cloudlets for deployment. Furthermore, it enables a non-dominated evaluation of solutions based on four objectives using crowding distance for selection. It provides an application-oriented solutions repair operator for repairing non-valid solutions and assuring that all solutions are generated in the feasible region. The proposed algorithm enables moving the cloudlets among pre-defined locations to increase the quality of service according to the change in the user density caused by user mobility. Comparing this developed algorithm with other algorithms shows its superiority in multi-objective optimization (MOO) evaluation metrics. VL-WIDE has provided the best in a number of non-dominated solutions and delta metrics and was competitive in other metrics.

关键词： optimization Edge computing Cloud computing whale optimization algorithms Costs Measurement Heuristic algorithms Mobile edge computing environment cloudlet deployment task offloading multi-objective optimization variable-length optimization

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