Efficient method using Whale Optimization Algorithm for reliability-based design optimization of labyrinth spillway

: 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.

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sEMG-Based Dynamic Muscle Fatigue Classification Using SVM with Improved Whale Optimization Algorithm

IEEE Internet of Things Journal ◽

10.1109/jiot.2021.3056126 ◽

2021 ◽

pp. 1-1

Author(s):

Quan Liu ◽

Yang Liu ◽

Congsheng Zhang ◽

Zhili Ruan ◽

Wei Meng ◽

...

Keyword(s):

Muscle Fatigue ◽

Optimization Algorithm ◽

Whale Optimization Algorithm ◽

Whale Optimization

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

Sensors ◽

10.3390/s21082628 ◽

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.

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

EURASIP Journal on Wireless Communications and Networking ◽

10.1186/s13638-021-02013-2 ◽

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.

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