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.

Determining Elastic Constants of Material Using Optimization Method and Vibration Test

2009 ◽  
Vol 628-629 ◽  
pp. 89-96
Author(s):  
Yu Hua Lin ◽  
Chia Lung Chang
Keyword(s):  
Simulated Annealing ◽  
Elastic Constants ◽  
Inverse Method ◽  
Simulated Annealing Algorithm ◽  
Steel Plate ◽  
Optimization Method ◽  
Coated Steel ◽  
Genetic Simulated Annealing Algorithm ◽  
Resonance Frequencies ◽  
Annealing Algorithm

This paper proposes an inverse method to obtain the elastic properties of material. The sum of the squared differences between the experimental resonance frequencies and calculated resonance frequencies from the finite element method is chosen as the objective function. The proposed method presents an optimization method, Hybrid Genetic /Simulated Annealing algorithm, to determine the elastic properties. When the objective function reaches its minimum value, its corresponding design variables are the elastic constants of the material. The inverse method is applied to determine the elastic constants of aluminum plate, Glass/PP laminate, and double coated steel plate .The results indicate that for few elastic constants as an aluminum plate, Hybrid Genetic /Simulated Annealing algorithm has no apparent improvement, but more calculation time in comparison method. While simulated annealing while for Glass/PP laminate and double coated steel plate with more elastic constants, Hybrid Genetic /Simulated Annealing algorithm is superior to the traditional simulated annealing method.

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.

scholarly journals Fault Localization Based on Hybrid Genetic Simulated Annealing Algorithm

2021 ◽  
Vol 28 (2) ◽  
pp. 101-109
Keyword(s):  
Simulated Annealing ◽  
Software Testing ◽  
Simulated Annealing Algorithm ◽  
Search Algorithm ◽  
Fault Localization ◽  
Good Effect ◽  
Test Case ◽  
Genetic Simulated Annealing Algorithm ◽  
Starting Point ◽  
Annealing Algorithm

Software testing is an important stage in the software development process, which is the key to ensure software quality and improve software reliability. Software fault localization is the most important part of software testing. In this paper, the fault localization problem is modeled as a combinatorial optimization problem, using the function call path as a starting point. A heuristic search algorithm based on hybrid genetic simulated annealing algorithm is used to locate software defects. Experimental results show that the fault localization method, which combines genetic algorithm, simulated annealing algorithm and function correlation analysis method, has a good effect on single fault localization and multi-fault localization. It greatly reduces the requirement of test case coverage and the burden of the testers, and improves the effect of fault localization.

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