Parameter Analysis and Optimization of Friction Pendulum Bearings in Underground Stations Based on Genetic Algorithm

Author(s):  
Zhiyi Chen ◽  
Peng Jia ◽  
Yifan Fan ◽  
Zhiqian Liu
2016 ◽  
Vol 836-837 ◽  
pp. 381-386
Author(s):  
Yan Hua Zhu

Rectangle part is the foundation of irregular part layout, about which domestic and overseas scholars have studied a lot and have put forward many algorithms. Based on a careful study of these algorithms in the paper, it is determined to solve the rectangle packing problem with genetic algorithm analysis. Different from the formerly used the genetic algorithm optimization layout, the algorithm of this paper stresses optimization localization rule in order to solve layout problem with the advantages of global searching ability of genetic algorithm. This algorithm sets the utilization ratio of the maximum area of capacity as the goal, after confirming the priority of deposition sequence of rectangle, in view of the locating rule of rectangle packing, based on feasible region and by introducing the method of attractive factors, optimizing the calculation for each parameter of placement function by utilizing genetic algorithm.Positioning function of the structure of this paper by changing the parameter value can cover the previous golden horn strategy, the lower left corner strategy, down the steps such as positioning method. Use VC programming to realize automatic two dimensional rectangular layout systems, the algorithm and example verification, the precision of parameter on the result of layout, the number of attractor for layout results, and the influence of parameter values for different rectangular piece of regularity. According to different rectangular block configuration, layout scheme can be better and faster.


Author(s):  
Lara del Val ◽  
María I. Jiménez ◽  
Mariano Raboso ◽  
Alberto Izquierdo ◽  
Juan J. Villacorta ◽  
...  

2017 ◽  
Vol 70 (1) ◽  
pp. 179-197
Author(s):  
Eugen Antal ◽  
Martin Eliáš

Abstract Evolutionary computation has represented a very popular way of problem solving in the recent years. This approach is also capable of effectively solving historical cipher in a fully automated way. This paper deals with empirical cryptanalysis of a monoalphabetic substitution using a genetic algorithm (GA) and a parallel genetic algorithm (PGA). The key ingredient of our contribution is the parameter analysis of GA and PGA. We focus on how these parameters affect the success rate of solving the monoalphabetic substitution.


Author(s):  
Darius Bethel ◽  
Hakki Erhan Sevil

The purpose of this study to analyze genetic algorithm (GA) and simulated an-nealing (SA) based approaches applied to well-known Traveling Salesman Prob-lem (TSP). As a NP-Hard problem, the goal of TSP is to find the shortest route possible to travel all the cities, given a set of cities and distances between cities. In order to solve the problem and achieve the optimal solution, all permutations need to be checked, which gets exponentially large as more cities are added. Our aim in this study is to provide comprehensive analysis of TSP solutions based on two methods, GA and SA, in order to find a near optimal solution for TSP. The re-sults of the simulations show that although the SA executed with faster comple-tion times comparing to GA, it took more iterations to find a solution. Additional-ly, GA solutions are significantly more accurate than SA solutions, where GA found a solution in relatively less iterations. The original contribution of this study is that GA based solution as well as SA based solution are developed to perform comprehensive parameter analysis. Further, a quantifiable comparison is provided for the results from each parameter analysis of GA and SA in terms of performance of solving TSP.


1994 ◽  
Vol 4 (9) ◽  
pp. 1281-1285 ◽  
Author(s):  
P. Sutton ◽  
D. L. Hunter ◽  
N. Jan

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