scholarly journals Parallel Genetic Algorithm and Parallel Simulated Annealing Algorithm for the Closest String Problem

2005 ◽  
pp. 591-597 ◽  
Author(s):  
Xuan Liu ◽  
Hongmei He ◽  
Ondrej Sýkora
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Parallel Genetic Algorithm ◽  
String Problem ◽  
Annealing Algorithm ◽  
Parallel Simulated Annealing

Optimal Training of Artificial Neural Networks to Forecast Power System State Variables

2014 ◽  
Vol 3 (1) ◽  
pp. 65-82 ◽  
Author(s):  
Victor Kurbatsky ◽  
Denis Sidorov ◽  
Nikita Tomin ◽  
Vadim Spiryaev
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Simulated Annealing ◽  
Power System ◽  
Simulated Annealing Algorithm ◽  
Active Power ◽  
Support Vector ◽  
State Variables ◽  
Annealing Algorithm ◽  
Artificial Neural

The problem of forecasting state variables of electric power system is studied. The paper suggests data-driven adaptive approach based on hybrid-genetic algorithm which combines the advantages of genetic algorithm and simulated annealing algorithm. The proposed method has two stages. At the first stage the input signal is decomposed into orthogonal basis functions based on the Hilbert-Huang transform. The genetic algorithm and simulated annealing algorithm are applied to optimal training of the artificial neural network and support vector machine at the second stage. The results of applying the developed approach for the short-term forecasts of active power flows in the electric networks are presented. The best efficiency of proposed approach is demonstrated on real retrospective data of active power flow forecast using the hybrid-genetic support vector machine algorithm.

Research on Active Polarization Control System of Fiber Laser Based on a Mixed Genetic Algorithm and Simulated Annealing Algorithm

2020 ◽  
Vol 40 (23) ◽  
pp. 2314002
Author(s):  
尤阳 You Yang ◽  
漆云凤 Qi Yunfeng ◽  
沈辉 Shen Hui ◽  
邹星星 Zou Xingxing ◽  
何兵 He Bing ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Control System ◽  
Fiber Laser ◽  
Simulated Annealing Algorithm ◽  
Polarization Control ◽  
Annealing Algorithm ◽  
Mixed Genetic Algorithm

scholarly journals A Comparison of Metaheuristic Optimization Algorithms for Scale Short-Form Development

2020 ◽  
Vol 80 (5) ◽  
pp. 910-931
Author(s):  
Anthony W. Raborn ◽  
Walter L. Leite ◽  
Katerina M. Marcoulides
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Short Form ◽  
Model Misspecification ◽  
External Variable ◽  
Monte Carlo Simulation Study ◽  
Short Forms ◽  
Annealing Algorithm ◽  
Form Development

This study compares automated methods to develop short forms of psychometric scales. Obtaining a short form that has both adequate internal structure and strong validity with respect to relationships with other variables is difficult with traditional methods of short-form development. Metaheuristic algorithms can select items for short forms while optimizing on several validity criteria, such as adequate model fit, composite reliability, and relationship to external variables. Using a Monte Carlo simulation study, this study compared existing implementations of the ant colony optimization, Tabu search, and genetic algorithm to select short forms of scales, as well as a new implementation of the simulated annealing algorithm. Selection of short forms of scales with unidimensional, multidimensional, and bifactor structure were evaluated, with and without model misspecification and/or an external variable. The results showed that when the confirmatory factor analysis model of the full form of the scale was correctly specified or had only minor misspecification, the four algorithms produced short forms with good psychometric qualities that maintained the desired factor structure of the full scale. Major model misspecification resulted in worse performance for all algorithms, but including an external variable only had minor effects on results. The simulated annealing algorithm showed the best overall performance as well as robustness to model misspecification, while the genetic algorithm produced short forms with worse fit than the other algorithms under conditions with model misspecification.

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