A New Hybrid Framework Based on Improved Genetic Algorithm and Simulated Annealing Algorithm for Optimization of Network IDS Based on BP Neural Network

2019 ◽  
pp. 507-521 ◽  
Author(s):  
Zouhair Chiba ◽  
Noreddine Abghour ◽  
Khalid Moussaid ◽  
Amina El omri ◽  
Mohamed Rida
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Bp Neural Network ◽  
Simulated Annealing Algorithm ◽  
Improved Genetic Algorithm ◽  
Hybrid Framework ◽  
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.

Process Parameters Optimization for Plasma Spraying Nanostructured ZrO2-7%Y2O3 Coating Based on Simulated Annealing Algorithm

2014 ◽  
Vol 1051 ◽  
pp. 12-16
Author(s):  
Bin Yang
Keyword(s):  
Neural Network ◽  
Simulated Annealing ◽  
Electric Current ◽  
Plasma Spraying ◽  
Process Parameters ◽  
Bp Neural Network ◽  
Bonding Strength ◽  
Simulated Annealing Algorithm ◽  
Gas Pressure ◽  
Annealing Algorithm

Process parameters of nanostructured ZrO2-7%Y2O3 coating during plasma spraying on the properties of the coating was optimized based on simulated annealing algorithm. BP neural network was applied to compute fitness of simulated annealing algorithm. A BP neural network model was built, four process parameters were input , the parameters included spraying distance, spraying electric current, primary gas pressure and secondary gas pressure, bonding strength of coating was output. Network was trained by orthogonal test data. Process parameters of coating were optimized by simulated annealing algorithm. The results show that maximal bonding strength of coating is 43.0377MPa. Process parameters for plasma spraying nanostructured ZrO2-7%Y2O3 coating are spraying distance of 80mm, spraying electric current of 977.0283A, primary gas pressure of 0.3046MPa and secondary gas pressure 0.9886MPa.

Damage Detection of Self-Anchored Suspension Bridge Based on Neural Network Model and Genetic-Simulated Annealing Algorithm

2011 ◽  
Vol 243-249 ◽  
pp. 1963-1967
Author(s):  
Qing Chen Zhang ◽  
Quan Sheng Sun
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Network Model ◽  
Neural Network Model ◽  
Simulated Annealing Algorithm ◽  
Suspension Bridge ◽  
Genetic Simulated Annealing Algorithm ◽  
Annealing Algorithm ◽  
Bp Neural Network Model

According to the characteristics of self-anchored suspension bridge, a new method to detect damage is introduced in this paper.It works in two stages.First, a BP neural network model is built to predict damaged position. Next, based on the characteristics of genetic algorithm and simulated annealing algorithm, a new approach, genetic-simulated annealing algorithm, is put forward to identify damage extent of detected positions. Compared with the traditional genetic algorithm, the global convergence effect of this algorithm is enhanced by using of the Metropolis acceptance rule of the simulated annealing algorithm in the searching process.

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