Research on Time Table Problem Based on Improved Genetic Algorithm Combined Chaos and Simulated Annealing Algorithm

2013 ◽  
Vol 13 (15) ◽  
pp. 2947-2952
Author(s):  
Dong Yunfeng
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Improved Genetic Algorithm ◽  
Time Table ◽  
Annealing Algorithm

scholarly journals Optimization and Design of Hammerheads and Fenders on Scrap Metal Shredders Based on Improved Genetic Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
He Tian ◽  
Guoqiang Wang ◽  
Kangkang Sun ◽  
Zeren Chen ◽  
Chuliang Yan ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Dynamic Balance ◽  
Optimal Solution ◽  
Scrap Metal ◽  
Improved Genetic Algorithm ◽  
Multiple Traveling Salesman Problem ◽  
Local Optimal Solution ◽  
Annealing Algorithm

Dynamic unbalance force is an important factor affecting the service life of scrap metal shredders (SMSs) as the product of mass error. Due to the complexity of hammerheads arrangement, it is difficult to take all the parts of the hammerhead into account in the traditional methods. A novel optimization algorithm combining genetic algorithm and simulated annealing algorithm is proposed to improve the dynamic balance of scrap metal shredders. The optimization of hammerheads and fenders on SMS in this paper is considered as a multiple traveling salesman problem (MTSP), which is a kind of NP-hard problem. To solve this problem, an improved genetic algorithm (IGA) combined with the global optimization characteristics of genetic algorithm (GA) and the local optimal solution of simulated annealing algorithm (SA) is proposed in this paper, which adopts SA in the process of selecting subpopulations. The optimization results show that the resultant force of the shredder central shaft by using IGA is less than the traditional metaheuristic algorithm, which greatly improves the dynamic balance of the SMS. Validated via ADAMS simulation, the results are in good agreement with the theoretical optimization analysis.

A novel evacuation path planning method based on improved genetic algorithm

2021 ◽  
pp. 1-11
Author(s):  
Longzhen Zhai ◽  
Shaohong Feng
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Path Planning ◽  
Simulated Annealing Algorithm ◽  
Planning Method ◽  
Improved Genetic Algorithm ◽  
Genetic Operator ◽  
Probability Operator ◽  
Annealing Algorithm ◽  
Evacuation Route

In order to solve the problem of finding the best evacuation route quickly and effectively, in the event of an accident, a novel evacuation route planning method is proposed based on Genetic Algorithm and Simulated Annealing algorithm in this paper. On the one hand, the simulated annealing algorithm is introduced and a simulated annealing genetic algorithm is proposed, which can effectively avoid the problem of the search process falling into the local optimal solution. On the other hand, an adaptive genetic operator is designed to achieve the purpose of maintaining population diversity. The adaptive genetic operator includes an adaptive crossover probability operator and an adaptive mutation probability operator. Finally, the path planning simulation verification is carried out for the genetic algorithm and the improved genetic algorithm. The simulation results show that the improved method has greatly improved the path planning distance and time compared with the traditional genetic algorithm.

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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