scholarly journals A Hybrid Modified Whale Optimization Algorithm with Simulated Annealing for Terrorism Prediction

2019 ◽  
Vol 24 (3) ◽  
pp. 281-287
Author(s):  
Ghada Soliman ◽  
Tarek Abou-El-Enien ◽  
Eid Emary ◽  
Motaz Khorshid
Keyword(s):  
Simulated Annealing ◽  
Optimization Algorithm ◽  
Whale Optimization Algorithm ◽  
Whale Optimization

scholarly journals Image Fusion Technique Based on Hybrid Whale Optimization Algorithm Simulated Annealing (hWOA-SA)

2019 ◽  
Vol 8 (11) ◽  
pp. 19-24
Keyword(s):  
Simulated Annealing ◽  
Image Fusion ◽  
Optimization Algorithm ◽  
Curvelet Transform ◽  
Whale Optimization Algorithm ◽  
Fusion Technique ◽  
Whale Optimization ◽  
Image Fusion Technique ◽  
Combined Algorithm

Curvelet transform is a multiscale directional transformer, which allows optimal non-adaptive sparse representation of object with edge. In this paper, a new image fusion technique has been developed by combination of whale optimization algorithm (WOA) and simulated annealing (SA) along with curvelet transform. The resulting combined algorithm is abbreviated as hybrid whale optimization algorithm with simulated annealing. Initially, hWOA-SA has been applied to enhancing the quality of image using de-noising scheme. Afterwards, the curvelet transform has been employed to carry out the fusion of images. In terms of PSNR, the curvelet transform exhibits the better performance. The effectiveness and validation of the proposed scheme has been carried-out using quality matrices. The performance analysis is carried out after checking the effectiveness of proposed approach by evaluating the various parameters such as: RSME, PFE, MAE, CORR, SNR, PSNR, MI, UQI and SSIM and compared with numerous techniques. Simulation results obtained from proposed hWOA-SA based image fusion are very competitive and better than other image fusion technique available in the literature.

scholarly journals Dynamic economic emission dispatch using whale optimization algorithm for multi-objective function

2021 ◽  
pp. 64-69
Author(s):  
M. F. Mehdi ◽  
A. Ahmad ◽  
S. S. Ul Haq ◽  
M. Saqib ◽  
M. F. Ullah
Keyword(s):  
Simulated Annealing ◽  
Differential Evolution ◽  
Optimization Algorithm ◽  
Optimal Solution ◽  
Whale Optimization Algorithm ◽  
Power Network ◽  
Multi Objective ◽  
Whale Optimization ◽  
Emission Dispatch ◽  
Economic Emission Dispatch

Introduction. Dynamic Economic Emission Dispatch is the extended version of the traditional economic emission dispatch problem in which ramp rate is taken into account for the limit of generators in a power network. Purpose. Dynamic Economic Emission Dispatch considered the treats of economy and emissions as competitive targets for optimal dispatch problems, and to reach a solution it requires some conflict resolution. Novelty. The decision-making method to solve the Dynamic Economic Emission Dispatch problem has a goal for each objective function, for this purpose, the multi-objective problem is transformed into single goal optimization by using the weighted sum method and then control/solve by Whale Optimization Algorithm. Methodology. This paper presents a newly developed metaheuristic technique based on Whale Optimization Algorithm to solve the Dynamic Economic Emission Dispatch problem. The main inspiration for this optimization technique is the fact that metaheuristic algorithms are becoming popular day by day because of their simplicity, no gradient information requirement, easily bypass local optima, and can be used for a variety of other problems. This algorithm includes all possible factors that will yield the minimum cost and emissions of a Dynamic Economic Emission Dispatch problem for the efficient operation of generators in a power network. The proposed approach performs well to perform in diverse problem and converge the solution to near best optimal solution. Results. The proposed strategy is validated by simulating on MATLAB® for 5 IEEE standard test system. Numerical results show the capabilities of the proposed algorithm to establish an optimal solution of the Dynamic Economic Emission Dispatch problem in a several runs. The proposed algorithm shows good performance over the recently proposed algorithms such as Multi-Objective Neural Network trained with Differential Evolution, Particle swarm optimization, evolutionary programming, simulated annealing, Pattern search, multi-objective differential evolution, and multi-objective hybrid differential evolution with simulated annealing technique.

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.

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