The spotted hyena optimization algorithm for weight-reduction of automobile brake components

2020 ◽  
Vol 62 (4) ◽  
pp. 383-388 ◽  
Author(s):  
Betül Sultan Yıldız
Keyword(s):  
Optimum Design ◽  
Optimization Algorithm ◽  
Weight Reduction ◽  
Optimization Methods ◽  
Spotted Hyena ◽  
Brake Pedal ◽  
Lion Algorithm ◽  
Metaheuristic Methods ◽  
Whale Optimization ◽  
Automobile Components

Abstract In recent years, metaheuristic methods have been preferred for the optimum design of automobile components, and important results have been accomplished. In this paper, a comparison of the whale optimization algorithm (WOA), the ant lion algorithm(ALO), and the spotted hyena optimization algorithm (SHOA) are presented to show how these optimization methods have been exploited to achieve weight reduction in an automobile brake pedal while maintaining stress requirements. This research is the first in the literature elucidating the application of the SHOA for the optimum design of automobile components. Optimization using the SHOA results in a reduction of 18.1 % of brake pedal weight.

Optimal design of differential mount using nature-inspired optimization methods

2021 ◽  
Vol 63 (8) ◽  
pp. 764-769
Author(s):  
Emre İsa Albak ◽  
Erol Solmaz ◽  
Ferruh Öztürk
Keyword(s):  
Optimum Design ◽  
Weight Reduction ◽  
Lightweight Design ◽  
Reduction Process ◽  
Optimization Methods ◽  
Convergence Speed ◽  
Structural Performance ◽  
Grey Wolf Optimizer ◽  
Solution Quality ◽  
Dragonfly Algorithm

Abstract Structural performance and lightweight design are a significant challenge in the automotive industry. Optimization methods are essential tools to overcome this challenge. Recently, nature-inspired optimization methods have been widely used to find optimum design variables for the weight reduction process. The objective of this study is to investigate the best differential mount design using nature-based optimum design techniques for weight reduction. The performances of the nature-based algorithms are tested using convergence speed, solution quality, and robustness to find the best design outlines. In order to examine the structural performance of the differential mount, static analyses are performed using the finite element method. In the first step of the optimization study, a sampling space is generated by the Latin hypercube sampling method. Then the radial basis function metamodeling technique is used to create the surrogate models. Finally, differential mount optimization is performed by using genetic algorithms (GA), particle swarm optimization (PSO), grey wolf optimizer (GWO), moth-flame optimization (MFO), ant lion optimizer (ALO) and dragonfly algorithm (DA), and the results are compared. All methods except PSO gave good and close results. Considering solution quality, robustness and convergence speed data, the best optimization methods were found to be MFO and ALO. As a result of the optimization, the differential mount weight is reduced by 14.6 wt.-% compared to the initial design.

Intelligent portfolio asset prediction enabled by hybrid Jaya-based spotted hyena optimization algorithm

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Meeta Sharma ◽  
Hardayal Singh Shekhawat
Keyword(s):  
Deep Learning ◽  
Portfolio Optimization ◽  
Optimization Algorithm ◽  
Spotted Hyena ◽  
Content Type ◽  
Whale Optimization ◽  
The Future ◽  
Asset Selection ◽  
Learning Concept ◽  
Better Than

Purpose The purpose of this study is to provide a novel portfolio asset prediction by means of the modified deep learning and hybrid meta-heuristic concept. In the past few years, portfolio optimization has appeared as a demanding and fascinating multi-objective problem, in the area of computational finance. Yet, it is accepting the growing attention of fund management companies, researchers and individual investors. The primary issues in portfolio selection are the choice of a subset of assets and its related optimal weights of every chosen asset. The composition of every asset is chosen in a manner such that the total profit or return of the portfolio is improved thereby reducing the risk at the same time. Design/methodology/approach This paper provides a novel portfolio asset prediction using the modified deep learning concept. For implementing this framework, a set of data involving the portfolio details of different companies for certain duration is selected. The proposed model involves two main phases. One is to predict the future state or profit of every company, and the other is to select the company which is giving maximum profit in the future. In the first phase, a deep learning model called recurrent neural network (RNN) is used for predicting the future condition of the entire companies taken in the data set and thus creates the data library. Once the forecasting of the data is done, the selection of companies for the portfolio is done using a hybrid optimization algorithm by integrating Jaya algorithm (JA) and spotted hyena optimization (SHO) termed as Jaya-based spotted hyena optimization (J-SHO). This optimization model tries to get the optimal solution including which company has to be selected, and optimized RNN helps to predict the future return while using those companies. The main objective model of the J-SHO-based RNN is to maximize the prediction accuracy and J-SHO-based portfolio asset selection is to maximize the profit. Extensive experiments on the benchmark datasets from real-world stock markets with diverse assets in various time periods shows that the developed model outperforms other state-of-the-art strategies proving its efficiency in portfolio optimization. Findings From the analysis, the profit analysis of proposed J-SHO for predicting after 7 days in next month was 46.15% better than particle swarm optimization (PSO), 18.75% better than grey wolf optimization (GWO), 35.71% better than whale optimization algorithm (WOA), 5.56% superior to JA and 35.71% superior to SHO. Therefore, it can be certified that the proposed J-SHO was effective in providing intelligent portfolio asset selection and prediction when compared with the conventional methods. Originality/value This paper presents a technique for providing a novel portfolio asset prediction using J-SHO algorithm. This is the first work uses J-SHO-based optimization for providing a novel portfolio asset prediction using the modified deep learning concept.

scholarly journals An Improved Nonlinear Grey Bernoulli Model Based on the Whale Optimization Algorithm and Its Application

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jianming Jiang ◽  
Ting Feng ◽  
Caixia Liu
Keyword(s):  
Optimization Algorithm ◽  
Structural Parameters ◽  
Optimization Methods ◽  
Whale Optimization Algorithm ◽  
Bernoulli Model ◽  
Proposed Model ◽  
Whale Optimization ◽  
Inherent Error ◽  
Applicable Range ◽  
The Difference

In order to improve the prediction performance of the existing nonlinear grey Bernoulli model and extend its applicable range, an improved nonlinear grey Bernoulli model is presented by using a grey modeling technique and optimization methods. First, the traditional whitening equation of nonlinear grey Bernoulli model is transformed into its linear formulae. Second, improved structural parameters of the model are proposed to eliminate the inherent error caused by the leap jumping from the differential equation to the difference one. As a result, an improved nonlinear grey Bernoulli model is obtained. Finally, the structural parameters of the model are calculated by the whale optimization algorithm. The numerical results of several examples show that the presented model’s prediction accuracy is higher than that of the existing models, and the proposed model is more suitable for these practical cases.

scholarly journals Automated Maintenance Data Classification Using Recurrent Neural Network: Enhancement by Spotted Hyena-Based Whale Optimization

Mathematics ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 2008
Author(s):  
Mustufa Haider Abidi ◽  
Usama Umer ◽  
Muneer Khan Mohammed ◽  
Mohamed K. Aboudaif ◽  
Hisham Alkhalefah
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Feature Selection ◽  
Data Acquisition ◽  
Recurrent Neural Network ◽  
Optimization Algorithm ◽  
Data Classification ◽  
Whale Optimization Algorithm ◽  
Spotted Hyena ◽  
Whale Optimization

Data classification has been considered extensively in different fields, such as machine learning, artificial intelligence, pattern recognition, and data mining, and the expansion of classification has yielded immense achievements. The automatic classification of maintenance data has been investigated over the past few decades owing to its usefulness in construction and facility management. To utilize automated data classification in the maintenance field, a data classification model is implemented in this study based on the analysis of different mechanical maintenance data. The developed model involves four main steps: (a) data acquisition, (b) feature extraction, (c) feature selection, and (d) classification. During data acquisition, four types of dataset are collected from the benchmark Google datasets. The attributes of each dataset are further processed for classification. Principal component analysis and first-order and second-order statistical features are computed during the feature extraction process. To reduce the dimensions of the features for error-free classification, feature selection was performed. The hybridization of two algorithms, the Whale Optimization Algorithm (WOA) and Spotted Hyena Optimization (SHO), tends to produce a new algorithm—i.e., a Spotted Hyena-based Whale Optimization Algorithm (SH-WOA), which is adopted for performing feature selection. The selected features are subjected to a deep learning algorithm called Recurrent Neural Network (RNN). To enhance the efficiency of conventional RNNs, the number of hidden neurons in an RNN is optimized using the developed SH-WOA. Finally, the efficacy of the proposed model is verified utilizing the entire dataset. Experimental results show that the developed model can effectively solve uncertain data classification, which minimizes the execution time and enhances efficiency.

scholarly journals Optimal Operation of Stand-Alone Microgrid Considering Emission Issues and Demand Response Program Using Whale Optimization Algorithm

2021 ◽  
Vol 13 (14) ◽  
pp. 7710
Author(s):  
Mehrdad Tahmasebi ◽  
Jagadeesh Pasupuleti ◽  
Fatemeh Mohamadian ◽  
Mohammad Shakeri ◽  
Josep M. Guerrero ◽  
...  
Keyword(s):  
Power Systems ◽  
Demand Response ◽  
Optimization Algorithm ◽  
New Technologies ◽  
Renewable Energy Sources ◽  
Optimization Methods ◽  
Optimal Operation ◽  
Whale Optimization Algorithm ◽  
Whale Optimization ◽  
Demand Response Program

Microgrids are new technologies for integrating renewable energies into power systems. Optimal operation of renewable energy sources in standalone micro-grids is an intensive task due to the continuous variation of their output powers and intermittant nature. This work addresses the optimum operation of an independent microgrid considering the demand response program (DRP). An energy management model with two different scenarios has been proposed to minimize the costs of operation and emissions. Interruptible/curtailable loads are considered in DRPs. Besides, due to the growing concern of the developing efficient optimization methods and algorithms in line with the increasing needs of microgrids, the focus of this study is on using the whale meta-heuristic algorithm for operation management of microgrids. The findings indicate that the whale optimization algorithm outperforms the other known algorithms such as imperialist competitive and genetic algorithms, as well as particle swarm optimization. Furthermore, the results show that the use of DRPS has a significant impact on the costs of operation and emissions.

scholarly journals Evaluation of the stability enhancement of the conventional sliding mode controller using whale optimization algorithm

2021 ◽  
Vol 21 (2) ◽  
pp. 744
Author(s):  
Aws Mahmood Abdullah ◽  
Ali Mohsin Kaittan ◽  
Mustafa Sabah Taha
Keyword(s):  
Optimization Algorithm ◽  
Sliding Mode ◽  
Optimization Methods ◽  
Whale Optimization Algorithm ◽  
Sliding Mode Controller ◽  
Whale Optimization ◽  
The Stability ◽  
Stability Enhancement ◽  
The Impact ◽  
The Relationship

The proposed work is an attempt to investigate the stability of the nonlinear system by using a whale optimization algorithm as of one of the meta-heuristic optimization methods, and this investigation was conducted on a single inverted pendulum as a study model. The evaluation measures which were used in this article values of gain and sliding surface of the conventional sliding mode controller to illustrate the extent of the system`s stability. Furthermore, control action, the relationship between error and its derivative, desired, and actual position in addition to sliding response graphically showed the feasibility of the proposed solution. The attained results illustrated considerable improvement in the settling time and minimizing the impact of chattering behavior.

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.

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