scholarly journals Optimal Design of Negative Stiffness Devices for Highway Bridges Using Performance-Based Genetic Algorithm

2021 ◽  
Vol 9 ◽  
Author(s):  
Sun Tong ◽  
Zhu Tianqi ◽  
Sun Li ◽  
Zhang Hao
Keyword(s):  
Genetic Algorithm ◽  
Parameter Optimization ◽  
Time History ◽  
Highway Bridges ◽  
Dynamic Responses ◽  
Negative Stiffness ◽  
Highway Bridge ◽  
Mutation Operators ◽  
Fitness Value ◽  
Benchmark Highway Bridge

Parameter optimization analysis on the negative stiffness device (NSD) installed in the benchmark highway bridge is carried out in this study. Key parameters and constrain conditions are determined in accordance with the characteristics of NSD, and an objective function is designed with safety and comfort being considered. Individual fitness value–related cross and mutation operators are designed to protect excellent chromosome and improve the efficiency and convergence of computation. The genetic algorithm is used to realize parameter optimization of the NSD used in a benchmark highway bridge. Dynamic responses of structure without NSD, with random NSD, and with optimal designed NSD are compared. By analyzing the time history of displacement and acceleration, it can be concluded that dynamic responses of the structure decrease obviously when the NSD is added, and a better seismic reduction effect can be reached when the NSD is designed optimally in accordance with the optimization method and different earthquake excitations have slight influence on the optimization results.

Optimization of Milling Parameters Based on Genetic Algorithm

2011 ◽  
Vol 141 ◽  
pp. 403-407
Author(s):  
Qing Li ◽  
Run Wang ◽  
Q.J. Liu ◽  
Rong Zong Wu ◽  
M.K. Shao ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Parameter Optimization ◽  
Cutting Parameters ◽  
Optimal Point ◽  
Milling Parameters ◽  
Fitness Value ◽  
Search Field ◽  
Hierarchical Genetic Algorithm ◽  
Search Domain

This paper presents a hierarchical genetic algorithm for interactive change search domain, using the search field continuously to shrink toward the optimal point, and also to optimize the multi-layer at the same time, conducting the interaction of individual between the layers. Individuals with the fitness value greater than zero are filtered out gradually as the most optimized cutting parameters. This method has greatly increased the time and accuracy of parameter optimization.

Optimum Design of Structures for Earthquake Induced Loading by Wavelet Neural Network

2007 ◽  
pp. 80-100
Author(s):  
Eysa Salajegheh ◽  
Ali Heidari
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Dynamic Analysis ◽  
Optimum Design ◽  
Low Frequency ◽  
Time History ◽  
Wavelet Neural Network ◽  
Dynamic Responses ◽  
Frequency Content ◽  
Fast Wavelet

Optimum design of structures for earthquake induced loading is achieved by a modified genetic algorithm (MGA). Some features of the simulated annealing (SA) are used to control various parameters of the genetic algorithm (GA). To reduce the computational work, a fast wavelet transform is used. The record is decomposed into two parts. One part contains the low frequency of the record, and the other contains the high frequency of the record. The low-frequency content is used for dynamic analysis. Then using a wavelet neural network, the dynamic responses of the structures are approximated. By such approximation, the dynamic analysis of the structure becomes unnecessary in the process of optimisation. The wavelet neural networks have been employed as a general approximation tool for the time history dynamic analysis. A number of structures are designed for optimal weight and the results are compared to those corresponding to the exact dynamic analysis.

scholarly journals Genetic Algorithm for lecturing schedule optimization

2019 ◽  
Vol 13 (1) ◽  
pp. 83 ◽  
Author(s):  
David Kristiadi ◽  
Rudy Hartanto
Keyword(s):  
Genetic Algorithm ◽  
Optimal Schedule ◽  
Optimal Solution ◽  
Computation Method ◽  
Scheduling Problems ◽  
Study Program ◽  
Directed Mutation ◽  
Classic Problem ◽  
Mutation Operators ◽  
Fitness Value

Scheduling is a classic problem in lecturing. Rooms, lecturers, times and scheduling constraints must be managed well to get an optimal schedule. University of Boyolali (UBY) also encounter the same scheduling problems. The problem was tried to be solved by building a library based on Genetic Algorithm (GA). GA is a computation method which inspired by natural selection. The computation consists of some operators i.e. Tournament Selection, Uniform Crossover, Weak Parent Replacement and two mutation operators (Interchanging Mutation and Violated Directed Mutation (VDM)). The two mutation method are compared to find which better mutation operator. The library was planned to have a capability to define custom constraints (scheduling requirements that were not accommodated by the library) without core program modifications. The test results show that VDM is more promising for optimal solutions than Interchanging Mutation. In UBY cases, optimal solution (fitness value=1) is reached in 12 minutes 41 second with adding 6 new room and inactivated 2 constraint i.e. lecturing begins at 14.00 except for 3rd semester of science law study program with morning class and lecturing participants must not over classroom capacity.

scholarly journals Cooperative Task Assignment of a Heterogeneous Multi-UAV System Using an Adaptive Genetic Algorithm

Electronics ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 687
Author(s):  
Fang Ye ◽  
Jie Chen ◽  
Yuan Tian ◽  
Tao Jiang
Keyword(s):  
Genetic Algorithm ◽  
Random Search ◽  
Task Assignment ◽  
Optimization Method ◽  
Adaptive Genetic Algorithm ◽  
Mutation Operators ◽  
Fitness Value ◽  
Coupling Constraints ◽  
Crossover And Mutation ◽  
And Task

The cooperative multiple task assignment problem (CMTAP) is an NP-hard combinatorial optimization problem. In this paper, CMTAP is to allocate multiple heterogeneous fixed-wing UAVs to perform a suppression of enemy air defense (SEAD) mission on multiple stationary ground targets. To solve this problem, we study the adaptive genetic algorithm (AGA) under the assumptions of the heterogeneity of UAVs and task coupling constraints. Firstly, the multi-type gene chromosome encoding scheme is designed to generate feasible chromosomes that satisfy the heterogeneity of UAVs and task coupling constraints. Then, AGA introduces the Dubins car model to simulate the UAV path formation and derives the fitness value of each chromosome. In order to comply with the chromosome coding strategy of multi-type genes, we designed the corresponding crossover and mutation operators to generate feasible offspring populations. Especially, the proposed mutation operators with the state-transition scheme enhance the stochastic searching ability of the proposed algorithm. Last but not least, the proposed AGA dynamically adjusts the number of crossover and mutation populations to avoid the subjective selection of simulation parameters. The numerical simulations verify that the proposed AGA has a better optimization ability and convergence effect compared with the random search method, genetic algorithm, ant colony optimization method, and particle search optimization method. Therefore, the effectiveness of the proposed algorithm is proven.

Machining Parameter Optimization of Bidirectional CFRP Composite Pipe by Genetic Algorithm

2014 ◽  
Vol 56 (9) ◽  
pp. 728-736 ◽  
Author(s):  
Krishnasamy Vijaykumar ◽  
Kavan Panneerselvam ◽  
Abdullah Naveen Sait
Keyword(s):  
Genetic Algorithm ◽  
Parameter Optimization ◽  
Composite Pipe ◽  
Cfrp Composite ◽  
Machining Parameter

Dynamic responses of highway bridge subjected to heavy truck impact

2021 ◽  
Vol 232 ◽  
pp. 111828
Author(s):  
Kai Heng ◽  
Ruiwen Li ◽  
Zhuoran Li ◽  
Hao Wu
Keyword(s):  
Dynamic Responses ◽  
Highway Bridge ◽  
Heavy Truck

A Two-Stage Hybrid Algorithm for Flexible Job-Shop Scheduling

2011 ◽  
Vol 268-270 ◽  
pp. 476-481
Author(s):  
Li Gao ◽  
Ke Lin Xu ◽  
Wei Zhu ◽  
Na Na Yang
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Algorithm ◽  
Job Shop ◽  
Flow Shop ◽  
Job Shop Scheduling ◽  
Selection Procedure ◽  
Dynamic Programming Method ◽  
Two Stage ◽  
Simulated Annealing Genetic Algorithm ◽  
Mutation Operators

A mathematical model was constructed with two objectives. A two-stage hybrid algorithm was developed for solving this problem. At first, the man-hour optimization based on genetic algorithm and dynamic programming method, the model decomposes the flow shop into two layers: sub-layer and patrilineal layer. On the basis of the man-hour optimization，A simulated annealing genetic algorithm was proposed to optimize the sequence of operations. A new selection procedure was proposed and hybrid crossover operators and mutation operators were adopted. A benchmark problem solving result indicates that the proposed algorithm is effective.

Performance Assessment of a Highway Bridge Structure employing Adaptive Negative Stiffness for Seismic Protection

2013 ◽  
Author(s):  
N. Attary ◽  
M. Symans ◽  
S. Nagarajaiah ◽  
A. M. Reinhorn ◽  
M. C. Constantinou ◽  
...  
Keyword(s):  
Performance Assessment ◽  
Negative Stiffness ◽  
Seismic Protection ◽  
Highway Bridge ◽  
Bridge Structure

scholarly journals Parameter Optimization of Three Phase Boost Inverter Using Genetic Algorithm for Linear Loads

2016 ◽  
Vol 90 ◽  
pp. 559-565 ◽  
Author(s):  
G. Arunkumar ◽  
I. Gnanambal ◽  
S. Naresh ◽  
P.C. Karthik ◽  
Jagadish Kumar Patra
Keyword(s):  
Genetic Algorithm ◽  
Parameter Optimization ◽  
Three Phase

Solving Vibration Control Problems Using Reduced Order Models for Flexible Structures: A Genetic Algorithm Approach

1997 ◽  
Author(s):  
Sourav Kundu ◽  
Kentaro Kamagata ◽  
Shigeru Sugino ◽  
Takeshi Minowa ◽  
Kazuto Seto
Keyword(s):  
Genetic Algorithm ◽  
Vibration Control ◽  
Controller Design ◽  
Fitness Function ◽  
Flexible Structures ◽  
Design Solution ◽  
Reduced Order Models ◽  
Reduced Order ◽  
Fitness Value ◽  
Genetic Algorithm Approach

Abstract A Genetic Algorithm (GA) based approach for solution of optimal control design of flexible structures is presented in this paper. The method for modeling flexible structures with distributed parameters as reduced-order models with lumped parameters, which has been developed previously, is employed. Due to some restrictions on controller design it is necessary to make a reduced-order model of the structure. Once the model is established the design of flexible structures is considered as a feedback search procedure where a new solution is assigned some fitness value for the GA and the algorithm iterates till some satisfactory design solution is achieved. We propose a pole assignment method to determine the evaluation (fitness) function to be used by the GA to find optimal damping ratios in passive elements. This paper demonstrates the first results of a genetic algorithm approach to solution of the vibration control problem for practical control applications to flexible tower-like structures.

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