Modeling and optimal design of LLC resonant converter using whale optimization algorithm

2018 ◽  
Vol 09 (06) ◽  
pp. 1850059
Author(s):  
K. S. Sarath ◽  
S. Sekar
Keyword(s):  
Parameter Optimization ◽  
Optimization Algorithm ◽  
Search Algorithm ◽  
Whale Optimization Algorithm ◽  
Switching Frequency ◽  
Resonant Converter ◽  
Switching Loss ◽  
Start Up ◽  
High Switching Frequency ◽  
Whale Optimization

Resonant converter (RC) was brought under research in the 80’s widely, which can attain very small switching loss, therefore, facilitating resonant topologies to function at the high switching frequency. It is well addressed in the review that the optimal parameterization of the resonant converter is a crucial task. While the literature has come out with different methodologies, they are highly conceptual and so the uncertainty due to high theoretical impact persists. This paper intends to develop a Parameter Optimization (PO) algorithm for designing and developing of LLC-RC. The proposed algorithm overwhelms the limitation by introducing a nonconceptual model based on the simulated outcome. Specifically, the resonant current under start-up conditions is acquired from the literary outcome, and the intelligent model is constructed. Based on the proposed model, a renowned search algorithm called as Whale Optimization Algorithm (WOA) is exploited to optimize the time constant of the resonant converter, which is a critical design parameter. The objective model is derived as a function of start-up time and so the start-up time can be minimized. Moreover, the response speed of the output voltage is also increased. The proposed Whale Optimization Algorithm based Parameter Optimization (WOAPO) is compared with the conventional techniques such as IAPO, Ant Bee Colony-PO (ABC-PO), Particle Swarm Optimization- PO (PSOPO), FireFly PO (FFPO) and Grey Wolf Optimization (GWOPO). The obtained result verifies the performance of the proposed method in modeling LLC-RC system.

scholarly journals Highly Precise Modified Blue Whale Method Framed by Blending Bat and Local Search Algorithm for the Optimality of Image Fusion Algorithm

2020 ◽  
Vol 2 (4) ◽  
pp. 195-208
Author(s):  
Sayantan Dutta ◽  
Ayan Banerjee
Keyword(s):  
Local Search ◽  
Image Fusion ◽  
Optimization Algorithm ◽  
Search Algorithm ◽  
Optimization Techniques ◽  
Whale Optimization Algorithm ◽  
Local Search Algorithm ◽  
Fusion Algorithm ◽  
Whale Optimization ◽  
Nature Inspired Algorithm

Image fusion has gained huge popularity in the field of medical and satellite imaging for image analysis. The lack of usages of image fusion is due to a deficiency of suitable optimization techniques and dedicated hardware. In recent days WOA (whale optimization algorithm) is gaining popularity. Like another straightforward nature-inspired algorithm, WOA has some problems in its searching process. In this paper, we have tried to improve the WOA algorithm by modifying the WOA algorithm. This MWOA (modified whale optimization algorithm) algorithm is amalgamed with LSA (local search algorithm) and BA (bat algorithm). The LSA algorithm helps the system to be faster, and BA algorithm helps to increase the accuracy of the system. This optimization algorithm is checked using MATLAB R2018b. Simulated using ModelSim, and the synthesizing is done using Xilinx Vivado 18.2 synthesis tool. The outcome of the simulation result and the synthesis result outshine other metaheuristic optimization algorithms.

scholarly journals Statistical Assessment of Whale Optimization Algorithm for Sizing and Placement of DG Considering Multi-Objective Function with PSO and CSA Algorithm

2020 ◽  
Vol 9 (5) ◽  
pp. 404-411
Keyword(s):  
Optimization Algorithm ◽  
Energy Demand ◽  
Power Plants ◽  
Search Algorithm ◽  
Optimization Techniques ◽  
Whale Optimization Algorithm ◽  
Optimal Placement ◽  
Critical System ◽  
Multi Objective ◽  
Whale Optimization

Distributed or decentralized power generation (DGEN) technology is popularized in the 21st century and it emerged has an effective alternative solution to meet the forecasted energy demand for restructured power system by putting restriction on power plants and transmission lines of the of the next decade generation. Modified state policies and increased technological innovation for low-capacity production promotes increased development and investment of DGEN. The use of distributed renewable energy generation has been driven by environmental concerns. DGEN's incorporation into the distribution side of the network offers critical system advantages such as voltage assistance support, reduction in loss, transmission power increase, strengthened system performance, etc. This paper presents the optimized DG placement (ODGP) and sizing solution in distribution side of the network for the multiobjective formulation includes the objective of minimizing the losses, maximization of voltage stability and also includes the cost requirement. The new Meta-heuristic based Approach named as Whale optimization algorithm is used for optimal placement and Sizing of DGEN is considered in this work and the solution of the proposed optimization algorithm is compared with two most popular optimization techniques such as Particle Swarm Optimization (PSOA), Cuckoo Search Algorithm (CSOA). The comparative analysis of these above said optimization techniques is developed and compared for performance comparison can be done with 69 bus IEEE standard radial system to validate the results of the proposed multi objective problem.

scholarly journals An Optimized Framework for Energy-Resource Allocation in a Cloud Environment based on the Whale Optimization Algorithm

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1583 ◽  
Author(s):  
Shanky Goyal ◽  
Shashi Bhushan ◽  
Yogesh Kumar ◽  
Abu ul Hassan S. Rana ◽  
Muhammad Raheel Bhutta ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Optimization Algorithm ◽  
Search Algorithm ◽  
Energy Resource ◽  
Whale Optimization Algorithm ◽  
Cloud Environment ◽  
Swarm Optimization ◽  
Cloud Technology ◽  
Whale Optimization

Cloud computing offers the services to access, manipulate and configure data online over the web. The cloud term refers to an internet network which is remotely available and accessible at anytime from anywhere. Cloud computing is undoubtedly an innovation as the investment in the real and physical infrastructure is much greater than the cloud technology investment. The present work addresses the issue of power consumption done by cloud infrastructure. As there is a need for algorithms and techniques that can reduce energy consumption and schedule resource for the effectiveness of servers. Load balancing is also a significant part of cloud technology that enables the balanced distribution of load among multiple servers to fulfill users’ growing demand. The present work used various optimization algorithms such as particle swarm optimization (PSO), cat swarm optimization (CSO), BAT, cuckoo search algorithm (CSA) optimization algorithm and the whale optimization algorithm (WOA) for balancing the load, energy efficiency, and better resource scheduling to make an efficient cloud environment. In the case of seven servers and eight server’s settings, the results revealed that whale optimization algorithm outperformed other algorithms in terms of response time, energy consumption, execution time and throughput.

scholarly journals Ajuste Ótimo de ESP e SVC-POD para Amortecimento de Oscilações

2021 ◽  
Author(s):  
Wesley Peres ◽  
Bruna C. Ferreira ◽  
Fabrício C. Gonçalves ◽  
Felipe L. S. Magalhães ◽  
Junior N. N. Costa ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Whale Optimization Algorithm ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Power Oscillation ◽  
Whale Optimization ◽  
Gravitational Search

O amortecimento de oscilações de potência é essencial na operação de sistemas de potência. Oscilações não amortecidas ou fracamente amortecidas podem limitar a capacidade de transferência de potência e causar blecautes. Para resolver esse problema, estabilizadores de sistemas de potência (ESP) instalados em geradores síncronos têm sido utilizados desde a década de setenta. Outra opção é utilizar um controlador denominado Power Oscillation Damper (POD) em dispositivos FACTS tais como o Compensador Estático de Reativos (CER). Com o objetivo de melhorar a estabilidade dos sistemas de potência, um projeto ótimo e robusto de ESP e POD deve ser realizado. Considerado as soluções de boa qualidade fornecidas por metaheurísticas, esse artigo compara quatro técnicas (Whale Optimization Algorithm, Grey Wolf Optimization, Gravitational Search Algorithm e Algoritmos Genéticos) na solução do problema de otimização mencionado. O ajuste de controladores ESP e POD é formulado como um problema de otimização com o objetivo de maximizar o coeficiente de amortecimento do autovalor dominante em malha fechada considerando vários pontos de operação para garantia de robustez. Resultados para um sistema de duas áreas são discutidos.

Performance Evaluation of Fiber Wireless (FiWi) Access Network using Position Optimization of ONUs

2020 ◽  
Vol 10 ◽  
Author(s):  
Nitin Chouhan ◽  
Uma Rathore Bhatt ◽  
Raksha Upadhyay
Keyword(s):  
Power Consumption ◽  
Optimization Algorithm ◽  
Optical Network ◽  
Access Network ◽  
Whale Optimization Algorithm ◽  
Wireless Access ◽  
Delivery Ratio ◽  
Packet Delivery ◽  
Whale Optimization ◽  
Wireless Access Network

: Fiber Wireless Access Network is the blend of passive optical network and wireless access network. This network provides higher capacity, better flexibility, more stability and improved reliability to the users at lower cost. Network component (such as Optical Network Unit (ONU)) placement is one of the major research issues which affects the network design, performance and cost. Considering all these concerns, we implement customized Whale Optimization Algorithm (WOA) for ONU placement. Initially whale optimization algorithm is applied to get optimized position of ONUs, which is followed by reduction of number of ONUs in the network. Reduction of ONUs is done such that with fewer number of ONUs all routers present in the network can communicate. In order to ensure the performance of the network we compute the network parameters such as Packet Delivery Ratio (PDR), Total Time for Delivering the Packets in the Network (TTDPN) and percentage reduction in power consumption for the proposed algorithm. The performance of the proposed work is compared with existing algorithms (deterministic and centrally placed ONUs with predefined hops) and has been analyzed through extensive simulation. The result shows that the proposed algorithm is superior to the other algorithms in terms of minimum required ONUs and reduced power consumption in the network with almost same packet delivery ratio and total time for delivering the packets in the network. Therefore, present work is suitable for developing cost-effective FiWi network with maintained network performance.

scholarly journals Multi-Objective Whale Optimization Algorithm for Computation Offloading Optimization in Mobile Edge Computing

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2628
Author(s):  
Mengxing Huang ◽  
Qianhao Zhai ◽  
Yinjie Chen ◽  
Siling Feng ◽  
Feng Shu
Keyword(s):  
Optimization Algorithm ◽  
Edge Computing ◽  
Solution Set ◽  
Whale Optimization Algorithm ◽  
Computation Offloading ◽  
Mobile Edge Computing ◽  
Multi Objective ◽  
Whale Optimization ◽  
Artificial Immune System Algorithm

Computation offloading is one of the most important problems in edge computing. Devices can transmit computation tasks to servers to be executed through computation offloading. However, not all the computation tasks can be offloaded to servers with the limitation of network conditions. Therefore, it is very important to decide quickly how many tasks should be executed on servers and how many should be executed locally. Only computation tasks that are properly offloaded can improve the Quality of Service (QoS). Some existing methods only focus on a single objection, and of the others some have high computational complexity. There still have no method that could balance the targets and complexity for universal application. In this study, a Multi-Objective Whale Optimization Algorithm (MOWOA) based on time and energy consumption is proposed to solve the optimal offloading mechanism of computation offloading in mobile edge computing. It is the first time that MOWOA has been applied in this area. For improving the quality of the solution set, crowding degrees are introduced and all solutions are sorted by crowding degrees. Additionally, an improved MOWOA (MOWOA2) by using the gravity reference point method is proposed to obtain better diversity of the solution set. Compared with some typical approaches, such as the Grid-Based Evolutionary Algorithm (GrEA), Cluster-Gradient-based Artificial Immune System Algorithm (CGbAIS), Non-dominated Sorting Genetic Algorithm III (NSGA-III), etc., the MOWOA2 performs better in terms of the quality of the final solutions.

scholarly journals An image denoising method based on BP neural network optimized by improved whale optimization algorithm

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Chunzhi Wang ◽  
Min Li ◽  
Ruoxi Wang ◽  
Han Yu ◽  
Shuping Wang
Keyword(s):  
Neural Network ◽  
Image Denoising ◽  
Optimization Algorithm ◽  
Bp Neural Network ◽  
Weight Coefficient ◽  
Whale Optimization Algorithm ◽  
Denoising Method ◽  
Training Time ◽  
Whale Optimization ◽  
City Construction

AbstractAs an important part of smart city construction, traffic image denoising has been studied widely. Image denoising technique can enhance the performance of segmentation and recognition model and improve the accuracy of segmentation and recognition results. However, due to the different types of noise and the degree of noise pollution, the traditional image denoising methods generally have some problems, such as blurred edges and details, loss of image information. This paper presents an image denoising method based on BP neural network optimized by improved whale optimization algorithm. Firstly, the nonlinear convergence factor and adaptive weight coefficient are introduced into the algorithm to improve the optimization ability and convergence characteristics of the standard whale optimization algorithm. Then, the improved whale optimization algorithm is used to optimize the initial weight and threshold value of BP neural network to overcome the dependence in the construction process, and shorten the training time of the neural network. Finally, the optimized BP neural network is applied to benchmark image denoising and traffic image denoising. The experimental results show that compared with the traditional denoising methods such as Median filtering, Neighborhood average filtering and Wiener filtering, the proposed method has better performance in peak signal-to-noise ratio.

Whale optimization algorithm based on lateral inhibition for image matching and vision-guided AUV docking

2020 ◽  
pp. 1-12
Author(s):  
Zheping Yan ◽  
Jinzhong Zhang ◽  
Jialing Tang
Keyword(s):  
Lateral Inhibition ◽  
Optimization Algorithm ◽  
Image Matching ◽  
Autonomous Underwater Vehicle ◽  
Optimal Solution ◽  
Search Space ◽  
Whale Optimization Algorithm ◽  
Inhibition Mechanism ◽  
Whale Optimization ◽  
Accuracy And Stability

The accuracy and stability of relative pose estimation of an autonomous underwater vehicle (AUV) and a target depend on whether the characteristics of the underwater image can be accurately and quickly extracted. In this paper, a whale optimization algorithm (WOA) based on lateral inhibition (LI) is proposed to solve the image matching and vision-guided AUV docking problem. The proposed method is named the LI-WOA. The WOA is motivated by the behavior of humpback whales, and it mainly imitates encircling prey, bubble-net attacking and searching for prey to obtain the globally optimal solution in the search space. The WOA not only balances exploration and exploitation but also has a faster convergence speed, higher calculation accuracy and stronger robustness than other approaches. The lateral inhibition mechanism can effectively perform image enhancement and image edge extraction to improve the accuracy and stability of image matching. The LI-WOA combines the optimization efficiency of the WOA and the matching accuracy of the LI mechanism to improve convergence accuracy and the correct matching rate. To verify its effectiveness and feasibility, the WOA is compared with other algorithms by maximizing the similarity between the original image and the template image. The experimental results show that the LI-WOA has a better average value, a higher correct rate, less execution time and stronger robustness than other algorithms. The LI-WOA is an effective and stable method for solving the image matching and vision-guided AUV docking problem.

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