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

Author(s):  
Quan Liu ◽  
Yang Liu ◽  
Congsheng Zhang ◽  
Zhili Ruan ◽  
Wei Meng ◽  
...  
Author(s):  
Nitin Chouhan ◽  
Uma Rathore Bhatt ◽  
Raksha Upadhyay

: 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.


Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2628
Author(s):  
Mengxing Huang ◽  
Qianhao Zhai ◽  
Yinjie Chen ◽  
Siling Feng ◽  
Feng Shu

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.


Author(s):  
Chunzhi Wang ◽  
Min Li ◽  
Ruoxi Wang ◽  
Han Yu ◽  
Shuping Wang

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.


2020 ◽  
pp. 1-12
Author(s):  
Zheping Yan ◽  
Jinzhong Zhang ◽  
Jialing Tang

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.


Symmetry ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 48
Author(s):  
Jin Zhang ◽  
Li Hong ◽  
Qing Liu

The whale optimization algorithm is a new type of swarm intelligence bionic optimization algorithm, which has achieved good optimization results in solving continuous optimization problems. However, it has less application in discrete optimization problems. A variable neighborhood discrete whale optimization algorithm for the traveling salesman problem (TSP) is studied in this paper. The discrete code is designed first, and then the adaptive weight, Gaussian disturbance, and variable neighborhood search strategy are introduced, so that the population diversity and the global search ability of the algorithm are improved. The proposed algorithm is tested by 12 classic problems of the Traveling Salesman Problem Library (TSPLIB). Experiment results show that the proposed algorithm has better optimization performance and higher efficiency compared with other popular algorithms and relevant literature.


Mathematics ◽  
2021 ◽  
Vol 9 (13) ◽  
pp. 1477
Author(s):  
Chun-Yao Lee ◽  
Guang-Lin Zhuo

This paper proposes a hybrid whale optimization algorithm (WOA) that is derived from the genetic and thermal exchange optimization-based whale optimization algorithm (GWOA-TEO) to enhance global optimization capability. First, the high-quality initial population is generated to improve the performance of GWOA-TEO. Then, thermal exchange optimization (TEO) is applied to improve exploitation performance. Next, a memory is considered that can store historical best-so-far solutions, achieving higher performance without adding additional computational costs. Finally, a crossover operator based on the memory and a position update mechanism of the leading solution based on the memory are proposed to improve the exploration performance. The GWOA-TEO algorithm is then compared with five state-of-the-art optimization algorithms on CEC 2017 benchmark test functions and 8 UCI repository datasets. The statistical results of the CEC 2017 benchmark test functions show that the GWOA-TEO algorithm has good accuracy for global optimization. The classification results of 8 UCI repository datasets also show that the GWOA-TEO algorithm has competitive results with regard to comparison algorithms in recognition rate. Thus, the proposed algorithm is proven to execute excellent performance in solving optimization problems.


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