Performance evaluation of artificial bee colony algorithm and its variants in the optimum design of steel skeletal structures

2020 ◽  
Vol 22 (1) ◽  
pp. 73-91
Author(s):  
M. P. Saka ◽  
I. Aydogdu
Keyword(s):  
Performance Evaluation ◽  
Optimum Design ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Skeletal Structures ◽  
Bee Colony

scholarly journals Performance Evaluation of Energy Transition Based on the Technique for Order Preference by a Similar to Ideal Solution and Support Vector Machine Optimized by an Improved Artificial Bee Colony Algorithm

Energies ◽  
2019 ◽  
Vol 12 (16) ◽  
pp. 3059 ◽  
Author(s):  
Zhen Li ◽  
Yun Li ◽  
Yanbin Li
Keyword(s):  
Support Vector Machine ◽  
Performance Evaluation ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Energy Transition ◽  
Indicator System ◽  
Support Vector ◽  
Ideal Solution ◽  
Bee Colony ◽  
Order Preference

Energy transition is an important factor when dealing with climate change and energy crisis under resource constraints. The performance evaluation of it is significant for improving and promoting the process of energy transition. This paper explores the application of the support vector machine improved by the artificial bee colony algorithm (IABC-SVM) method in the energy transition performance evaluation process. It provides an intelligent evaluation tool for the evaluation of the regional energy transition performance. Firstly, the evaluation indicator system of energy transition is constructed from five dimensions: energy supply, demand, efficiency, institution, and environment. Then, the technique for order preference by a similar to ideal solution improved by a combination weighting (CW-TOPSIS) method and IABC-SVM are constructed. After that, according to the evaluation values of 30 provinces in China calculated by CW-TOPSIS, 10-fold cross validation is used to compare the errors of support vector machine (SVM), support vector machine optimized by the artificial bee colony algorithm (ABC-SVM), and IABC-SVM, which proves the effectiveness and accuracy of IABC-SVM in evaluating the performance of energy transition. Finally, the IABC-SVM is used to evaluate the energy transition performance of 30 provinces in 2016. Through a comparative analysis, the relevant suggestions of energy transition are put forward.

Thermal Based Optimum Design of Tapered Roller Bearing Through Evolutionary Algorithm

2013 ◽  
Author(s):  
Rajiv Tiwari ◽  
Rahul Chandran
Keyword(s):  
Evolutionary Algorithm ◽  
Optimum Design ◽  
High Speed ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Roller Bearing ◽  
Maximum Temperature ◽  
Dynamic Capacity ◽  
Bee Colony ◽  
Tapered Roller

In high-speed applications the maximum temperature in bearings are a crucial concern. In some applications the bearing is the prime source of heat, the temperature at which a bearing operates dictates the type and amount of lubricant and the material for the fabrication of the bearing components. In the present work a thermal based optimum design of tapered roller bearings has been presented. Internal geometry of the bearing has been optimized based by evolutionary algorithm. Constraints are geometrical, kinematical, strength and thermal in nature. Optimum designs have been found to have better performance parameters. Artificial bee colony algorithm has been used for the present optimization problem, for solving constrained non-linear optimization formulations. A total of nine design variables corresponding to the bearing geometry and constraint factors have been considered. A convergence study has been carried and optimum designs based on temperature is compared with the optimized values based on dynamic capacity, both using artificial bee colony algorithm. There is an excellent improvement found in the optimized bearing designs based on temperature when compared with the optimized results based on dynamic capacity in respect of the maximum temperature in the bearing with the artificial bee colony algorithm.

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