Optimization for Crashworthiness of Urban Transit Trains Using Genetic Algorithm

2011 ◽  
Vol 66-68 ◽  
pp. 1167-1172 ◽  
Author(s):  
Zhuo Jun Xie ◽  
Ping Xu ◽  
Yu Qi Luo
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problems ◽  
Urban Transit ◽  
Design Variables ◽  
Specific Goal ◽  
Design Function ◽  
Crash Model ◽  
Current Energy ◽  
Force Characteristics

As it is tough for the current energy absorb devices of urban vehicles to meet the crashworthiness requirements in the collision scenario of 25km/h, a methodology to improve the general crashworthiness is presented. A multi-criteria optimization, with the deformations and accelerations of all cars as the design functions and the force characteristics of end structures of cars as design variables, is defined and the Pareto Fonts are obtained. Then defining energy absorbed as design function, a single criteria optimization is made and the specific goal is achieved. No explicit relationship could be found between the design variables and the design functions, so a crash model of a train with velocity of 25km/h colliding to another train stopped is built and the genetic algorithm is chosen to solve the optimization problems. The results indicate that the crashworthiness performance of the trains is significantly improved and the crashworthiness requirements could be reached finally.

Optimization Study of a Hybrid Solar Air Conditioner

2013 ◽  
Author(s):  
Ali Al-Alili ◽  
Yunho Hwang ◽  
Reinhard Radermacher
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problems ◽  
Global Optimum ◽  
Optimized Design ◽  
Air Conditioner ◽  
Air Conditioners ◽  
Multi Objective Genetic Algorithm ◽  
Design Variables ◽  
Optimization Study ◽  
Systems Simulation

In order for the solar air conditioners (A/Cs) to become a real alternative to the conventional systems, their performance and total cost has to be optimized. In this study, an innovative hybrid solar A/C was simulated using the transient systems simulation (TRNSYS) program, which was coupled with MATLAB in order to carry out the optimization study. Two optimization problems were formulated with the following design variables: collector area, collector mass flow rate, storage tank volume, and number of batteries. The Genetic Algorithm (GA) was selected to find the global optimum design for the lowest electrical consumption. To optimize the two objective functions simultaneously, a Multi-Objective Genetic Algorithm (MOGA) was used to find the Pareto front within the design variables’ bounds while satisfying the constraints. The optimized design was also compared to a standard vapor compression cycle. The results show that coupling TRNSYS and MATLAB expands TRNSYS optimization capability in solving more complicated optimization problems.

scholarly journals Using a Goal-Switching Selection Operator in Multi-Objective Genetic Algorithm Optimization Problems

2013 ◽  
Author(s):  
Daniel Shaefer ◽  
Scott Ferguson
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problems ◽  
Pareto Frontier ◽  
Product Line ◽  
Algorithm Optimization ◽  
Nsga Ii ◽  
Solution Quality ◽  
Design Variables ◽  
Mp3 Player ◽  
Selection Operator

This paper demonstrates how solution quality for multiobjective optimization problems can be improved by altering the selection phase of a multiobjective genetic algorithm. Rather than the traditional roulette selection used in algorithms like NSGA-II, this paper adds a goal switching technique to the selection operator. Goal switching in this context represents the rotation of the selection operator among a problem’s various objective functions to increase search diversity. This rotation can be specified over a set period of generations, evaluations, CPU time, or other factors defined by the designer. This technique is tested using a set period of generations before switching occurs, with only one objective considered at a time. Two test cases are explored, the first as identified in the Congress on Evolutionary Computation (CEC) 2009 special session and the second a case study concerning the market-driven design of a MP3 player product line. These problems were chosen because the first test case’s Pareto frontier is continuous and concave while being relatively easy to find. The second Pareto frontier is more difficult to obtain and the problem’s design space is significantly more complex. Selection operators of roulette and roulette with goal switching were tested with 3 to 7 design variables for the CEC 09 problem, and 81 design variables for the MP3 player problem. Results show that goal switching improves the number of Pareto frontier points found and can also lead to improvements in hypervolume and/or mean time to convergence.

Solar Hybrid Air Conditioner: Model Validation and Optimization

2016 ◽  
Vol 138 (3) ◽  
Author(s):  
Ali Al-Alili ◽  
Yunho Hwang ◽  
Reinhard Radermacher
Keyword(s):  
Genetic Algorithm ◽  
Energy Storage ◽  
Solar Collector ◽  
Optimization Problems ◽  
Electrical Energy ◽  
Global Optimum ◽  
Air Conditioner ◽  
Solar Thermal Energy ◽  
Design Variables ◽  
Optimization Study

Solar air conditioners (A/Cs) have attracted much attention in research, but their performance and cost have to be optimized in order to become a real alternative to conventional A/C systems. In this study, a hybrid solar A/C is simulated using the transient systems simulation program(trnsys), which is coupled with matlab in order to carry out the optimization study. The trnsys model is experimentally validated prior to the optimization study. Two optimization problems are formulated with the following design variables: solar collector area, solar collector mass flow rate, solar thermal energy storage volume, and solar electrical energy storage size. The genetic algorithm (GA) is selected to solve the single-objective optimization problem and find the global optimum design for the lowest electrical consumption. To optimize the two objective functions simultaneously, energy consumption and total cost (TC), a multi-objective genetic algorithm (MOGA) is used to find the Pareto curve within the design variables' bounds while satisfying the constraints. The overall cost of the optimized solar A/C design is also compared to a standard vapor compression cycle (VCC). The results show that coupling trnsys and matlab expands trnsys optimization capability in solving more complex optimization problems. The results also show that the optimized solar hybrid A/C is not very competitive when the electricity prices are low and no governmental support is provided.

scholarly journals Large-Scale Truss Topology and Sizing Optimization by an Improved Genetic Algorithm with Multipoint Approximation

2021 ◽  
Vol 12 (1) ◽  
pp. 407
Author(s):  
Tianshan Dong ◽  
Shenyan Chen ◽  
Hai Huang ◽  
Chao Han ◽  
Ziqi Dai ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Large Scale ◽  
Optimization Problems ◽  
Approximation Technique ◽  
Discrete Topology ◽  
Improved Genetic Algorithm ◽  
Approximation Accuracy ◽  
Design Variables ◽  
Metaheuristic Methods ◽  
Multipoint Approximation

Truss size and topology optimization problems have recently been solved mainly by many different metaheuristic methods, and these methods usually require a large number of structural analyses due to their mechanism of population evolution. A branched multipoint approximation technique has been introduced to decrease the number of structural analyses by establishing approximate functions instead of the structural analyses in Genetic Algorithm (GA) when GA addresses continuous size variables and discrete topology variables. For large-scale trusses with a large number of design variables, an enormous change in topology variables in the GA causes a loss of approximation accuracy and then makes optimization convergence difficult. In this paper, a technique named the label–clip–splice method is proposed to improve the above hybrid method in regard to the above problem. It reduces the current search domain of GA gradually by clipping and splicing the labeled variables from chromosomes and optimizes the mixed-variables model efficiently with an approximation technique for large-scale trusses. Structural analysis of the proposed method is extremely reduced compared with these single metaheuristic methods. Numerical examples are presented to verify the efficacy and advantages of the proposed technique.

scholarly journals A comparative evaluation of genetic and gradient-based algorithms applied to aerodynamic optimization

2008 ◽  
pp. 103-126
Author(s):  
David W. Zingg ◽  
Marian Nemec ◽  
Thomas H. Pulliam
Keyword(s):  
Genetic Algorithm ◽  
Shape Optimization ◽  
Comparative Evaluation ◽  
Pareto Front ◽  
Optimization Problems ◽  
Aerodynamic Optimization ◽  
Design Variables ◽  
Gradient Based ◽  
Degree Of Convergence ◽  
Multipoint Optimization

A genetic algorithm is compared with a gradient-based (adjoint) algorithm in the context of several aerodynamic shape optimization problems. The examples include singlepoint and multipoint optimization problems, as well as the computation of a Pareto front. The results demonstrate that both algorithms converge reliably to the same optimum. Depending on the nature of the problem, the number of design variables, and the degree of convergence, the genetic algorithm requires from 5 to 200 times as many function evaluations as the gradientbased algorithm.

scholarly journals An Improved Genetic Algorithm for the Optimization of Composite Structures

2006 ◽  
Author(s):  
Vladimir Gantovnik ◽  
Georges Fadel ◽  
Zafer Gu¨rdal
Keyword(s):  
Genetic Algorithm ◽  
Composite Structures ◽  
Optimization Problems ◽  
Multivariate Approximation ◽  
Approximation Procedure ◽  
Continuous Variables ◽  
Improved Genetic Algorithm ◽  
Design Variables ◽  
Discrete Design Variables ◽  
Proposed Modification

This paper describes a new approach for reducing the number of the fitness and constraint function evaluations required by a genetic algorithm (GA) for optimization problems with mixed continuous and discrete design variables. The proposed modification improves the efficiency of the memory constructed in terms of the continuous variables. The work presents the algorithmic implementation of the proposed memory scheme and demonstrates the efficiency of the proposed multivariate approximation procedure for the weight optimization of a segmented open cross section composite beam subjected to axial tension load. Results are generated to demonstrate the advantages of the proposed improvements to a standard genetic algorithm.

Multi-Objective Optimum Design of Rotor-Bearing Systems With Dynamic Constraints Using Immune-Genetic Algorithm

1999 ◽  
Author(s):  
Byung-Gun Choi ◽  
Bo-Suk Yang
Keyword(s):  
Genetic Algorithm ◽  
Immune System ◽  
Optimization Problems ◽  
Q Factor ◽  
Immune Genetic Algorithm ◽  
Dynamic Constraints ◽  
Design Variables ◽  
Resonance Response ◽  
Rotor Bearing ◽  
Combined Algorithm

Abstract An immune system has powerful abilities such as memory, recognition and learning how to respond to invading antigens, and is applying to many engineering algorithms in recent year. In this paper, the combined optimization algorithm (Immune-Genetic Algorithm: IGA) is proposed for multi-optimization problems by introducing the capability of the immune system that controls the proliferation of clones to the genetic algorithm. The new combined algorithm is applied to minimize the total weight of the shaft and the resonance response (Q factor), and to yield the critical speeds as far from the operating speed as possible. These factors play very important roles in designing a rotor-bearing system under the dynamic behavior constraints. The shaft diameter, the bearing length and clearance are chosen as the design variables. The dynamic characteristics are determined by applying the generalized FEM. The results show that the combined algorithm can reduce the weight of the shaft and improve the critical speed and Q factor with dynamic constraints.

Topology and Sizing Optimization of Micromixers Using Graph-Theoretical Representation and Genetic Algorithm

2017 ◽  
Author(s):  
Mitsuo Yoshimura ◽  
Koji Shimoyama ◽  
Takashi Misaka ◽  
Shigeru Obayashi
Keyword(s):  
Genetic Algorithm ◽  
Topology Optimization ◽  
Optimization Problems ◽  
Computational Cost ◽  
Kriging Model ◽  
Local Optimum ◽  
Sizing Optimization ◽  
Local Optima ◽  
Novel Approach ◽  
Design Variables

This paper proposes a novel approach for fluid topology optimization using genetic algorithm. In this study, the enhancement of mixing in the passive micromixers is considered. The efficient mixing is achieved by the grooves attached on the bottom of the microchannel and the optimal configuration of grooves is investigated. The grooves are represented based on the graph theory. The micromixers are analyzed by a CFD solver and the exploration by genetic algorithm is assisted by the Kriging model in order to reduce the computational cost. Three cases with different constraint and treatment for design variables are considered. In each case, GA found several local optima since the objective function is a multi-modal function and each local optimum revealed the specific characteristic for efficient mixing in micromixers. Moreover, we discuss the validity of the constraint for optimization problems. The results show a novel insight for design of micromixer and fluid topology optimization using genetic algorithm.

Axial Turbine Blade Aerodynamic Optimization Using a Novel Multi-Level Genetic Algorithm

2008 ◽  
Author(s):  
O¨zhan O¨ksu¨z ◽  
I˙brahim Sinan Akmandor
Keyword(s):  
Genetic Algorithm ◽  
Gas Turbine ◽  
Turbine Blade ◽  
Optimization Problems ◽  
Design Method ◽  
Turbine Blades ◽  
Optimization Method ◽  
Test Cases ◽  
Aerodynamic Optimization ◽  
Design Variables

In this paper, a new multiploid genetic optimization method handling surrogate models of the CFD solutions is presented and applied for single objective turbine blade aerodynamic optimization problem. A fast, efficient, robust, and automated design method is developed to aerodynamically optimize 3D gas turbine blades. The design objectives are selected as maximizing the adiabatic efficiency and torque so as to reduce the weight, size and cost of the gas turbine engine. A 3-Dimensional steady Reynolds Averaged Navier Stokes solver is coupled with an automated unstructured grid generation tool. The solver is verified using two well known test cases. Blade geometry is modeled by 36 design variables plus the number of blades variable in a row. Fine and coarse grid solutions are respected as high and low fidelity models, respectively. One of the test cases is selected as the baseline and is modified by the design process. It was found that the multiploid genetic algorithm successfully accelerates the optimization at the initial generations for both optimization problems, while preventing converging to local optimums.

scholarly journals Sizing and Topology Optimization of Trusses Using Genetic Algorithm

Materials ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 715
Author(s):  
Ingrid Delyová ◽  
Peter Frankovský ◽  
Jozef Bocko ◽  
Peter Trebuňa ◽  
Jozef Živčák ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problems ◽  
Point Of View ◽  
Truss Structures ◽  
And Topology ◽  
Design Variables ◽  
Structural Engineer ◽  
Topology Optimization Of Trusses ◽  
Structural Responses ◽  
Genetic Algorithm Approach

Genetic algorithms are a robust method for a solution of wide variety optimization problems. It explores a big space of design variables in order to find the best solution. From the point of view of a user, the algorithm requires the encoding of design variables into the form of strings and the procedure of optimization uses them for optimization. Here, for the structural engineer, it is crucial to find the form of objective function including the constraints of the task and also to avoid critical states during the solution of structural responses. This paper presents the use of genetic algorithm for solving truss structures. The use of genetic algorithm approach is shown on three cases of truss structures.

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