Simulated Annealing Algorithm Optimization of the Deterred-Coating Propellant Charge Design

2012 ◽  
Vol 496 ◽  
pp. 99-103
Author(s):  
Zhi Tao Liu ◽  
Dan Dan Ji ◽  
Bin Xu ◽  
Hong Xia Jia ◽  
Xin Liao
Keyword(s):  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Chamber Pressure ◽  
Mass Ratio ◽  
Algorithm Optimization ◽  
Ballistic Performance ◽  
Muzzle Velocity ◽  
Propellant Charge ◽  
Annealing Algorithm ◽  
Charge Design

In order to get better interior ballistic performance of deterred-coating propellant charge (DCPC), the simulated annealing algorithm optimization of DCPC carries out in this paper. The interior ballistic model of DCPC and its model of simulated annealing algorithm optimization are established based on the characteristics of deterred-coating propellant. Two key parameters of DCPC, the mass ratio of deterred-coating propellants to total propellants and the average perforation pressure of deterred-coating propellant，are optimized for the search of the maximum muzzle velocity under the restricting conditions of maximum chamber pressure and the maximum relative end point of combustion. The optimization results agree well with the standard charge parameters, which justifies the feasibility of the model.

Free Parameter Optimization of DTMDs Based on Improved Hybrid Genetic-Simulated Annealing Algorithm

2020 ◽  
Vol 20 (03) ◽  
pp. 2050031
Author(s):  
Qiang Han ◽  
Xuan Zhang ◽  
Kun Xu ◽  
Xiuli Du
Keyword(s):  
Simulated Annealing ◽  
Parameter Optimization ◽  
Free Parameter ◽  
Simulated Annealing Algorithm ◽  
Optimization Method ◽  
Design Parameters ◽  
Mass Ratio ◽  
Tuned Mass Dampers ◽  
Genetic Simulated Annealing Algorithm ◽  
Annealing Algorithm

The optimum design of distributed tuned mass dampers (DTMDs) is normally based on predefined restrictions, such as the location and/or mass ratio of the tuned mass dampers (TMDs). To further improve the control performance, a free parameter optimization method (FPOM) is proposed. This method only restricts the total mass of the DTMDs system and takes the installation position, mass ratio, stiffness and damping of each TMD as parameters to be optimized. An improved hybrid genetic-simulated annealing algorithm (IHGSA) is adopted to find the optimum values of the design parameters. This algorithm can solve the non-convexity and multimodality problems of the objective function and is quite effective in dealing with the large amount of computations in the free parameter optimization. A numerical benchmark model is adopted to compare the control efficiency of FPOM with conventional control scenarios, such as single TMD, multiple TMDs and DTMDs optimized through conventional methods. The results show that the DTMDs system optimized by using FPOM is superior to the other control scenarios for the same value of mass ratio.

Query Optimization Based on the Simulated Annealing and Particle Swarm Optimization

2012 ◽  
Vol 229-231 ◽  
pp. 1870-1873
Author(s):  
Ren Jie Song ◽  
Yan Wang
Keyword(s):  
Simulated Annealing ◽  
Query Optimization ◽  
Simulated Annealing Algorithm ◽  
Particle Swarm ◽  
Optimization Method ◽  
Particle Swarm Algorithm ◽  
Algorithm Optimization ◽  
Optimal Value ◽  
Global Optimal ◽  
Annealing Algorithm

In order to allow the user to quickly and accurately search the required information, a query optimization method based on a simulated annealing and particle swarm hybrid algorithm is proposed. The basic idea is: the query population into two flat sub populations, a sub population by using simulated annealing algorithm optimization, another sub populations by using particle swarm algorithm optimization, comparison of two adaptive values, to find the global optimal value. The experimental results show that the mixed algorithm, can further improve the precision and recall of query optimization.

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