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):  
Optimization Algorithm ◽  
Weight Reduction ◽  
Spotted Hyena

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.

scholarly journals Spotted Hyena Optimization Algorithm With Simulated Annealing for Feature Selection

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 71943-71962 ◽  
Author(s):  
Heming Jia ◽  
Jinduo Li ◽  
Wenlong Song ◽  
Xiaoxu Peng ◽  
Chunbo Lang ◽  
...  
Keyword(s):  
Feature Selection ◽  
Simulated Annealing ◽  
Optimization Algorithm ◽  
Spotted Hyena

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.

Solving Traveling Salesman Problem using Hybridization of Rider Optimization and Spotted Hyena Optimization Algorithm

2021 ◽  
pp. 115353
Author(s):  
Madugula Murali Krishna ◽  
Nibedan Panda ◽  
Santosh Kumar Majhi
Keyword(s):  
Traveling Salesman Problem ◽  
Optimization Algorithm ◽  
Traveling Salesman ◽  
Spotted Hyena

Hybrid spotted hyena–Nelder-Mead optimization algorithm for selection of optimal machining parameters in grinding operations

2021 ◽  
Vol 63 (3) ◽  
pp. 293-298
Author(s):  
Nantiwat Pholdee ◽  
Vivek K. Patel ◽  
Sadiq M. Sait ◽  
Sujin Bureerat ◽  
Ali Rıza Yıldız
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Spotted Hyena ◽  
Optimization Approach ◽  
Machining Parameters ◽  
Grinding Operations ◽  
Selection Of

Abstract In this research, a novel optimization algorithm, which is a hybrid spotted hyena-Nelder-Mead optimization algorithm (HSHO-NM) algorithm, has been introduced in solving grinding optimization problems. A well-known grinding optimization problem is solved to prove the superiority of the HSHO-NM over other algorithms. The results of the HSHO-NM are compared with others. The results show that HSHO-NM is an efficient optimization approach for obtaining the optimal manufacturing variables in grinding operations.

A Novel Spotted Hyena Optimization algorithm for MPPT under Partial Shading Conditions

2021 ◽  
Author(s):  
K.T Swetha ◽  
Venugopal Reddy Barry ◽  
Abin Robinson ◽  
Rohit Kumar Jain
Keyword(s):  
Optimization Algorithm ◽  
Spotted Hyena ◽  
Partial Shading

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.

ED-SHO: A framework for solving nonlinear economic load power dispatch problem using spotted hyena optimizer

2018 ◽  
Vol 33 (40) ◽  
pp. 1850239 ◽  
Author(s):  
Gaurav Dhiman ◽  
Sen Guo ◽  
Satnam Kaur
Keyword(s):  
Optimization Algorithm ◽  
Economic Dispatch ◽  
Spotted Hyena ◽  
Test Systems ◽  
Metaheuristic Optimization ◽  
Load Power ◽  
Power Dispatch ◽  
Metaheuristic Optimization Algorithm ◽  
Effectiveness And Efficiency

This paper presents the application of recently developed metaheuristic optimization algorithm, the spotted hyena optimizer, for solving both convex and non-convex economic dispatch problems. The proposed algorithm has been tested on various test systems (i.e. 6, 10, 20, and 40 generators systems) and compared with other well-known approaches to demonstrate its effectiveness and efficiency. The results show that the proposed algorithm is able to solve economic load power dispatch problem and converge toward the optimum with low computational efforts.

Prediction of Gene Selection Features Using Improved Multi-objective Spotted Hyena Optimization Algorithm

2019 ◽  
pp. 59-67 ◽  
Author(s):  
S. Divya ◽  
Eranki L. N. Kiran ◽  
Madhu Sudana Rao ◽  
Pujitha Vemulapati
Keyword(s):  
Optimization Algorithm ◽  
Gene Selection ◽  
Spotted Hyena ◽  
Multi Objective
Sign in / Sign up

Export Citation Format

Share Document