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IEEE PHOTONICS JOURNAL 2023年第1期15卷 1页

作者： Pham, Van Huan Chon, Byong Hyuk Ahn, Hee Kyung Korea Res Inst Stand & Sci Adv Instrumentat Inst Opt Imaging & Metrol Team Daejeon 34113 South Korea

Reflective Fourier ptychographic microscopy has much potential for industrial surface inspection due to the ability to overcome the physical limits of the numerical aperture of the optical microscopy. However, the time cost for misalignment calibration and Fourier ptychography (FP) recovery has been a big issue for industrial applications which require fast output. Here, we introduce a misalignment estimation method which is accelerated through the whale optimization algorithm by running in parallel on Central Processing Units (CPUs), named pWOA, to reduce computing time. The proposed method shows more accurate and faster calibration compared to other population-based algorithms, including the parallel genetic algorithm and the parallel particle swarm optimization, and much faster than that of the exhaustive search both in simulations and in real experiments. In addition, this cost-effective technique can address global non-convex optimization problems with heavy loss functions including reflective FP.

关键词： Light emitting diodes optimization whale optimization algorithms Statistics Sociology Parallel processing Mirrors Microscopy technologies for computing other imaging technique

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Indoor Visible Light Positioning Based on Improved whale optimization Method With Min-Max Algorithm

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2023年 72卷 1页

作者： Liu, Ren Liang, Zhonghua Wang, Zhenyu Li, Wei Changan Univ Sch Informat Engn Dept Commun Engn Xian 710064 Peoples R China

Recently, the Min-Max algorithm has been widely used in various indoor positioning systems due to its simplicity and robustness. In this article, an improved whale optimization algorithm (IWOA) associated with the Min-Max algorithm is proposed to improve the positioning accuracy concerning the original Min-Max algorithm for visible light positioning (VLP) systems. In the proposed algorithm, the three-dimensional (3-D) region of interest (RoI) affiliated with the target node (TN) is first obtained using the original Min-Max algorithm, and then the IWOA is employed to find the accurate position of the TN in the 3-D RoI with the lowest fitness value. In software simulations, the signal-to-noise ratio (SNR) value of the VLP system is 15 dB and the standard deviation of measured distance noise is 1 m. Our software simulation results show that in the case of two-dimensional (2-D) positioning, the averaged positioning error (APE) of the proposed IWOA-Min-Max algorithm is decreased by 84.6%, 52.1%, 1.9%, and 19.93%, respectively, for the original Min-Max algorithm, the extended Min-Max algorithms, the whale optimization algorithm (WOA), and the least square estimation (LSE). Furthermore, in the case of 3-D positioning, it is also reduced by 81.7%, 75.4%, 8.5%, and 63.9%, respectively, compared with that of the above four existing algorithms. Finally, hardware-in-the-loop simulation (HILS) results are also provided to verify the effectiveness of the proposed IWOA-Min-Max algorithm.

关键词： Statistics Sociology Three-dimensional displays whale optimization algorithms Partitioning algorithms Estimation LED lamps Improved whale optimization algorithm (IWOA) min-max visible light positioning (VLP)

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Dynamic Reactive Power optimization Strategy for AC/DC Hybrid Power Grid Considering Different Wind Power Penetration Levels

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IEEE ACCESS 2024年 12卷 187471-187482页

作者： Feng, Nan Feng, Yuyao Su, Yun Zhang, Yajun Niu, Tao State Grid Shanghai Elect Power Co Ltd Elect Power Res Inst Shanghai 200437 Peoples R China Chongqing Univ Elect Engn Dept Chongqing 400044 Peoples R China

With the increasing capacity of wind power integration and the further development of AC/DC hybrid power grids, the problem of voltage stability in the receiving end has become increasingly severe. The installation of dynamic reactive power compensation devices is a feasible measure to improve the voltage stability of the receiving end power grid. This paper proposes a dynamic reactive power optimization strategy for AC/DC hybrid power grids considering the integration of new energy sources. Firstly, a detailed mathematical model of the AC/DC hybrid system is established. In order to enhance the voltage restoration capability after a fault, the definition of a fault recovery indicator is provided. Subsequently, an optimal scheduling strategy for dynamic reactive power compensation devices is formulated to maximize the value of the fault recovery indicator. Considering the nonlinearity and non-convexity of the optimization model, trajectory sensitivity method and whale optimization algorithm are adopted to enhance the solution efficiency. Finally, an improved IEEE-9 test system is employed to demonstrate the effectiveness and feasibility of the proposed approach.

关键词： Reactive power Power system dynamics Mathematical models Transient analysis Power system stability Steady-state Power grids AC-DC power converters Wind power generation whale optimization algorithms Fault detection AC/DC hybrid power grids wind power dynamic reactive power fault recovery indicator trajectory sensitivity method whale optimization algorithm

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Structural optimization Design of the Rotary Blowout Preventer Shell Based on an Adaptive Sand Cat and whale optimization Algorithm

Structural Optimization Design of the Rotary Blowout Prevent...

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IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)

作者： Tian Haijiang Houpin Yoong Bih Lii Chua Ismail Saad Institute of Intelligent Manufacturing Fujian Polytechnic of Information Technology Fuzhou China Faculty of Engineering Universiti Malaysia Sabah Kota Kinabalu Malaysia

ISBN: (数字)9798350389692

ISBN: (纸本)9798350389708

A critical component of the Rotary Blowout Preventer (RBOP) is the rotating assembly, essential for its “drill-in circulation” functionality. The bottom of the outer cylinder of the rotating assembly endures significant hydrostatic pressure, leading to substantial deformation. This study establishes a finite element analysis model of the outer cylinder of the rotating assembly in ANSYS Workbench and validates the strength of the assembly shell by analyzing the stress distribution. The radii of curvature at the interface between the rotating assembly and the clamp, along with five shell thicknesses at different locations along the outer cylinder, are designated as independent variables. To minimize the mass of the shell, a mathematical model is formulated using Von Mises stress as the state variable. optimization computations are conducted using the Adaptive Sand Cat and whale optimization Algorithm (SCWOA). By adjusting the radii of curvature, the concentration of maximum stress is reduced, optimizing the stress distribution, thinning the wall thickness, and reducing the outer cylinder's weight. Compared to optimization results using the hybrid penalty function in ANSYS Workbench, SCWOA demonstrates faster convergence, achieving near-optimal solutions in fewer iterations, making it suitable for addressing challenging engineering optimization problems. Furthermore, the optimized results provide a theoretical foundation for subsequent repair and remanufacturing studies.

关键词： Deformation Maintenance engineering whale optimization algorithms Mathematical models Finite element analysis Clamps optimization Assembly Stress Convergence

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Reducing Task Scheduling Time in Cloud Computing using Novel Improved whale optimization Algorithm over Ant Colony Algorithm

Reducing Task Scheduling Time in Cloud Computing using Novel...

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Trends in Quantum Computing and Emerging Business Technologies (TQCEBT), International Conference on

作者： P. Sushma Priya S. John Justin Thangaraj Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

To decrease Cloud Computing Task Scheduling Time utilising Novel Improved whale Optimisation Algorithm over Ant Colony Algorithm. A Novel Improved whale Optimisation Algorithm schedules tasks in cloud computing. Cloud computing involves internet-based distribution of data storage, servers, databases, networking, and applications. Task scheduling data is obtained using cloudsim. We analyse, create, and implement cloud computing application framework and cloud computing infrastructures. After data import, Improved whale Optimisation and Ant Colony Algorithm are tried. Two algorithms have two groups. Each group has 120 samples with 80% G power, 0.05% threshold, and 95% CI. Task Scheduling Time (ms) is returned for inputs. The findings come from IBM SPSS. Results indicate a significant independent sample t test (p= 0.000, p<0.05). The algorithms vary statistically. Ant Colony takes longer to schedule tasks than Improved whale Optimisation Algorithm. According to study, the Improved whale Optimisation Algorithm (53.6000 MS) has a shorter Task Scheduling Time than the Ant Colony Algorithm (93.1000 MS).

关键词： Cloud computing Schedules Quantum computing Processor scheduling whales Scheduling whale optimization algorithms

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A Hybrid Multiagent Adaptive Clustering Algorithm Using whale optimization in VANETs Network

A Hybrid Multiagent Adaptive Clustering Algorithm Using Whal...

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Communication and Networks (ASIANComNet), Asian Conference on

作者： Zahraa Saad Abdulali Hussein Al-Aboudy S. Sri Nandhini Kowsalya Fatima H. Alsalamy Maysam Reyad Hadi Mohammed Ihsan National University of Science and Technology Dhi Qar Iraq Department of Computer Techniques Engineering Mazaya University College DhiQar Iraq Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai India Medical Laboratories Techniques Department Al-Mustaqbal University Hillah Iraq Department of Computer Science Altoosi University College Najaf Iraq Department of Computer Technical Engineering College of Technical Engineering The Islamic University Najaf Iraq

ISBN: (数字)9798350367003

ISBN: (纸本)9798350367225

Mainly to improve the efficiency to reduce the power utilization in earlier researches the clustering model is concentrated. It is the formation of clusters in the network which increases the connection degree fundamentally and with the leaders the network stability is also increased. With the presence of a huge number of devices even the earlier clustering models consists of certain drawbacks like data loss and delay. Overcome such drawbacks in this article a hybrid multiagent adaptive algorithm with whale optimization (HMACWO) is developed so that an optimal clustering is introduced which is able to increase the efficiency of the vehicular network. The core modules which are present in this article are an efficient clustering process and whale optimization with an improved clustering model. Experimental demonstration of this model is done in NS3 software and using certain parameters such as the cluster efficiency, CH lifetime, packet delivery ratio, network throughput, and average delay the performance of the network is analyzed. From the results, it is shown that the HMACWO attains a maximum cluster efficiency and $\mathbf{C H}$ lifetime when compared with the earlier methods.

关键词： Adaptation models Adaptive systems Clustering algorithms Throughput whale optimization algorithms Stability analysis Software Hybrid power systems Delays optimization

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An Effectual Strategy to Augment the Hello Interval of Each Node Using Enhanced Humpback whales optimization

An Effectual Strategy to Augment the Hello Interval of Each ...

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Artificial Intelligence and Emerging Technology (Global AI Summit), International Conference on

作者： Richa Purohit T. Saravanan Rashmi Dharwadkar Y Vahidhabanu S.P. Sasirekha N. Krishnaveni School of Computer Studies Sri Balaji University Pune India Dept. of CSE GITAM School of Technology GITAM (Deemed to be University) Bengaluru India Dept. of Computer Science Sri Balaji University Pune India Department of Computer Science and Engineering Kongunadu college of Engineering and Technology Trichy India Department of Computer Science and Engineering Karpagam Academy of Higher Education Coimbatore India Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798350379716

ISBN: (纸本)9798350379723

Wireless networks are highly utilized, especially Mobile Ad-hoc Networks (MANETs) for real-time data transmission. However, current routing protocols for MANETs face challenges that limit their effectiveness. Design issues include minimizing energy usage and ensuring node security. A commonly used routing protocol, AODV, involves periodic broadcasting of hello messages to monitor link connectivity and maintain routing tables. When link failure occurs due to node mobility, hello messages are broadcasted more frequently, which consumes more node energy and increases network overhead. To address this issue, this research proposes an Enhanced Humpback whales Optimized Routing Approach (EHWORA) that calculates an optimal hello interval based on a node's energy and mobility using an adaptive fuzzy inference system. This approach involves optimizing the hello interval of AODV routing through the use of an adaptive fuzzy inference system. Each node can dynamically adjust the transmission of hello messages based on the fuzzy-based hello interval. Simulation results demonstrate that ORA reduces network overhead, end-to-end delay, hello message fraction, average energy consumption, packet delivery ratio, and improves throughput compared to a recent method. The proposed protocol is evaluated using MATLAB software. To assess the validity of the research, we utilized MATLAB software to compare the proposed method with a recent approach. During the simulation, the proposed protocol depleted the energy of 3 nodes after 10 seconds, 5 nodes after 20 seconds, 9 nodes after 30 seconds, 11 nodes after 40 seconds, and 13 nodes after 50 seconds, respectively.

关键词： Fuzzy logic Adaptive systems Routing whale optimization algorithms Ad hoc networks Software Routing protocols MATLAB Artificial intelligence Mobile computing

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Feasibility and optimization of Solar Systems for Academic Institutions: A Multi-Algorithm Approach

Feasibility and Optimization of Solar Systems for Academic I...

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Sustainable Technologies for Industry 4.0 (STI), International Conference on

作者： Md. Ashaduzzaman Niloy M M Naushad Ali Md Shahadat Hossen Bibekananda Nath Md Ashraful Islam Shahela Akter Ahmed Al Mansur Department of Electrical and Electronic Engineering Green University of Bangladesh Bangladesh Department of ECE Gannon University Pennsylvania

ISBN: (数字)9798331531973

ISBN: (纸本)9798331531980

Renewable energy systems (RES) present a sustainable and resilient alternative to traditional single-source energy systems, offering improved safety and cost-effectiveness in power generation. This study investigates the efficiency and economic viability of a photovoltaic (PV)-based energy system with integrated energy storage, tailored for a university campus setting. To optimize the utilization of available space, advanced optimization algorithms- Grey Wolf Optimizer (GWO), whale optimization Algorithm (WOA), and Artificial Hummingbird Al-gorithm (AHA) were employed to identify the optimal system size and associated costs. The findings highlight the strong potential of RES in urban environments. Specifically, the case study on the Green University of Bangladesh campus demonstrates the feasibility of the proposed system, achieving a cost of energy (COE) of $0.018SlkWh and a payback period of 8 years and 10 months. This research illustrates the capacity of PV-based systems to provide reliable and sustainable energy to urban campuses, delivering significant economic and environmental advantages that advocate for their wider adoption in similar settings.

关键词： Photovoltaic systems Renewable energy sources Costs Urban areas Green products whale optimization algorithms Solar system Safety Reliability optimization

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Optimal Trajectory Planning for Robotic Arm Based on Improved whale optimization Algorithm

Optimal Trajectory Planning for Robotic Arm Based on Improve...

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Service Robotics (ICoSR), International Conference on

作者： Zhiqiang Ni Guirong Wang Hengchu Ye College of Mechanical and Electrical Engineering China Jiliang University Hangzhou China

ISBN: (数字)9798350367119

ISBN: (纸本)9798350367126

An integrated optimal trajectory planning approach is suggested to increase the shock resilience and operational efficiency of industrial robots. First, the refractive reverse learning mechanism, Lévy flights, and nonlinear convergence factor are incorporated to the basic whale optimization algorithm to create the improved whale optimization algorithm (IWOA). Then the superiority and efficacy of the improved algorithm are confirmed using the CEC2017 test function set. Subsequently, trajectory planning in the robot joint space utilizing 3-5-3 polynomial functions determines the whole optimization objective function in terms of time, impact, and energy consumption. When the joint motion curves derived from the improved algorithm are continuous and devoid of sudden shifts, the efficacy of the trajectory planning approach has finally been verified.

关键词： Energy consumption Trajectory planning Service robots Electric shock Manipulators whale optimization algorithms Polynomials Robots optimization Resilience

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Adaptive Swarm Intelligence-Based optimization Approach for Smart Grids Power Dispatch

Adaptive Swarm Intelligence-Based Optimization Approach for ...

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Emerging Technologies in Electronics, Computing and Communication (ICETECC), International Conference on

作者： Um-E-Habiba Alvi Ijaz Ahmed Syed Rizwan Hasan Babar Ashfaq Muhammad Raza Sana Mukhtar Department of Electrical Engineering PIEAS Islamabad Pakistan Department of Mechanical Engineering GIKI Topi KPK Pakistan Department of Economics PIDE Islamabad Pakistan Department of Electrical Engineering Sir Syed CASE Institute of Technology Islamabad Pakistan

Approximately 70–90 percent of today’s energy usage is derived from fossil fuels. Due to global warming and countless air pollutants, the atmosphere is polluted, compelling us to use new computer paradigms for efficient dispatching of smart energy grids. In order to solve the difficult nonlinear, non-convex, and constrained confined EEDs, a new whale optimization algorithm (WOA) is devised for the minimization of total cost and emission. The proposed method takes into account the social behaviour of humpback whales, with an emphasis on the nature of suggested meta-heuristic optimization algorithms. The proposed optimization paradigm generated data demonstrating AWSOA superiority to other advanced approaches. To ensure the efficacy of the proposed method, we compare it to other approaches and consider a number of technological constraints; the findings demonstrate that our approach is both cost-effective and accurate. In addition, the research assists electric energy firms in controlling emissions as required by emission regulatory agencies and saving the annual operational cost of thermal plants.

关键词： Costs Metaheuristics whales Atmosphere Minimization whale optimization algorithms Global warming

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