scholarly journals Improved Genetic Algorithm with Two-Level Approximation for Truss Optimization by Using Discrete Shape Variables

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Shen-yan Chen ◽  
Xiao-fang Shui ◽  
Dong-fang Li ◽  
Hai Huang
Keyword(s):  
Genetic Algorithm ◽  
Computational Cost ◽  
Approximation Problem ◽  
Optimization Method ◽  
Truss Optimization ◽  
Discrete Topology ◽  
Improved Genetic Algorithm ◽  
Design Variables ◽  
Fitness Value ◽  
Update Strategy

This paper presents an Improved Genetic Algorithm with Two-Level Approximation (IGATA) to minimize truss weight by simultaneously optimizing size, shape, and topology variables. On the basis of a previously presented truss sizing/topology optimization method based on two-level approximation and genetic algorithm (GA), a new method for adding shape variables is presented, in which the nodal positions are corresponding to a set of coordinate lists. A uniform optimization model including size/shape/topology variables is established. First, a first-level approximate problem is constructed to transform the original implicit problem to an explicit problem. To solve this explicit problem which involves size/shape/topology variables, GA is used to optimize individuals which include discrete topology variables and shape variables. When calculating the fitness value of each member in the current generation, a second-level approximation method is used to optimize the continuous size variables. With the introduction of shape variables, the original optimization algorithm was improved in individual coding strategy as well as GA execution techniques. Meanwhile, the update strategy of the first-level approximation problem was also improved. The results of numerical examples show that the proposed method is effective in dealing with the three kinds of design variables simultaneously, and the required computational cost for structural analysis is quite small.

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.

Optimization of Wind Turbine Airfoil With Good Stall Characteristics by Genetic Algorithm Using CFD and Neural Network

2013 ◽  
Author(s):  
Mohammad H. Djavareshkian ◽  
Amir Latifi
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Wind Turbine ◽  
Stokes Equations ◽  
Computational Cost ◽  
Numerical Procedure ◽  
Optimization Method ◽  
Navier Stokes ◽  
Design Variables ◽  
Wind Turbine Airfoil

In this research, optimization of a wind turbine airfoil is done by Genetic Algorithm (GA) as optimization method, coupled with CFD (Computational Fluid Dynamics) and Artificial Neural Network (ANN). A pressure-based implicit procedure is used to solve the Navier-Stokes equations on a nonorthogonal mesh with collocated finite volume formulation to calculate the aerodynamic coefficients. The boundedness criteria for the numerical procedure are determined from Normalized Variable Diagram (NVD) scheme and the k–ε eddy-viscosity turbulence model is utilized. ANN has been used as surrogate model to reduce computational cost and time. Single objective and multi objective optimization of wind turbine airfoil have been performed and the results of optimization are presented. To reduce the number of design variables and producing a smooth shaped airfoil, modified Hicks-Henne functions are used. In this process, the Eppler E387 airfoil has been applied as the base airfoil. Angle of attack varies from 0 to 20 degrees and Reynolds number of the flow is 460000.

Structural Optimization of Composite Panel with Lamination Parameters and Equivalent Stiffness Laminate Method

2014 ◽  
Vol 496-500 ◽  
pp. 429-435
Author(s):  
Xiao Ping Zhong ◽  
Peng Jin
Keyword(s):  
Genetic Algorithm ◽  
Structural Optimization ◽  
Composite Structure ◽  
Optimization Procedure ◽  
Optimization Method ◽  
Composite Panel ◽  
Analysis Tool ◽  
Equivalent Stiffness ◽  
Lamination Parameters ◽  
Design Variables

Firstly, a two-level optimization procedure for composite structure is investigated with lamination parameters as design variables and MSC.Nastran as analysis tool. The details using lamination parameters as MSC.Nastran input parameters are presented. Secondly, with a proper equivalent stiffness laminate built to substitute for the lamination parameters, a two-level optimization method based on the equivalent stiffness laminate is proposed. Compared with the lamination parameters-based method, the layer thicknesses of the equivalent stiffness laminate are adopted as continuous design variables at the first level. The corresponding lamination parameters are calculated from the optimal layer thicknesses. At the second level, genetic algorithm (GA) is applied to identify an optimal laminate configuration to target the lamination parameters obtained. The numerical example shows that the proposed method without considering constraints of lamination parameters can obtain better optimal results.

scholarly journals Sizing and shape optimization material use in 10 bar trusses

2021 ◽  
Vol 343 ◽  
pp. 04004
Author(s):  
Nenad Petrović ◽  
Nenad Kostić ◽  
Vesna Marjanović ◽  
Ileana Ioana Cofaru ◽  
Nenad Marjanović
Keyword(s):  
Genetic Algorithm ◽  
Shape Optimization ◽  
Cross Sections ◽  
Optimization Model ◽  
Structural Characteristics ◽  
Optimization Method ◽  
Truss Optimization ◽  
Algorithm Optimization ◽  
Sizing Optimization ◽  
Bill Of Materials

Truss optimization has the goal of achieving savings in costs and material while maintaining structural characteristics. In this research a 10 bar truss was structurally optimized in Rhino 6 using genetic algorithm optimization method. Results from previous research where sizing optimization was limited to using only three different cross-sections were compared to a sizing and shape optimization model which uses only those three cross-sections. Significant savings in mass have been found when using this approach. An analysis was conducted of the necessary bill of materials for these solutions. This research indicates practical effects which optimization can achieve in truss design.

Research on Coordination of Evacuation of Human and Car Simulation Optimization Method

2011 ◽  
Vol 130-134 ◽  
pp. 2185-2189
Author(s):  
Yun Long Ma ◽  
Jian Wang
Keyword(s):  
Genetic Algorithm ◽  
Simulation Optimization ◽  
Optimization Method ◽  
Crowd Simulation ◽  
Optimal Scheduling ◽  
Regular Pattern ◽  
Improved Genetic Algorithm ◽  
Whole Process ◽  
Evacuation Strategy ◽  
Coordination System

Among researches on people evacuation under emergency, there are no deep touch of coordinating evacuation strategy of human and traffic, and also can not set models and simulate the whole process of evacuation. This research focused on the human evacuation under emergencies, divided the situations into Not Intervene and May Intervene, analyzed the coordination system and probed the model of coordinating evacuation. Based on the result of crowd simulation, the author could forecast the result of human evacuation and find the optimal traffic schedule proposal. The author took use of the improved Genetic Algorithm as well as the distributed coordinating simulation, finally found a systematic optimal scheduling program and revealed the internal regular pattern of coordinating evacuation.

An Improved Hybrid Genetic Algorithm and Performance Study

2012 ◽  
Vol 482-484 ◽  
pp. 95-98
Author(s):  
Wei Dong Ji ◽  
Ke Qi Wang
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Optimization Method ◽  
Convergence Speed ◽  
Test Results ◽  
Improved Genetic Algorithm ◽  
Performance Study ◽  
Speed Up ◽  
And Performance

Put forward a kind of the hybrid improved genetic algorithm of particle swarm optimization method (PSO) combine with and BFGS algorithm of, this method using PSO good global optimization ability and the overall convergence of BFGS algorithm to overcome the blemish of in the conventional algorithm slow convergence speed and precocious and local convergence and so on. Through the three typical high dimensional function test results show that this method not only improved the algorithm of the global search ability, to speed up the convergence speed, but also improve the quality of the solution and its reliability of optimization results.

Improved Genetic Algorithm Based on Operation Order Matrix Encoding for Job Shop Scheduling Problem

2011 ◽  
Vol 189-193 ◽  
pp. 4212-4215
Author(s):  
Hong Zhan ◽  
Jian Jun Yang ◽  
Lu Yan Ju
Keyword(s):  
Genetic Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Improved Genetic Algorithm ◽  
Shop Scheduling ◽  
Order Matrix ◽  
Fitness Value ◽  
Matrix Encoding

This paper presents an improved genetic algorithm for the job shop scheduling problem. We designed a new encoding method based on operation order matrix, a matrix correspond to a chromosome, the value of elements is not repetitive, that means a processing order number in all operations of all jobs. Aiming at the features of the matrix encoding, we designed the crossover and mutation methods based on jobs, and the infeasible solutions are avoided. Through adjusting the computing method of fitness value, the improved genetic algorithm takes on some self adapting capability. The proposed approach is tested on some standard instances and compared with two other approaches. The computation results validate the algorithm is efficient.

The Optimization Design of Wind Turbine Gearbox Based on Improved Genetic Algorithm and Feasibility Analysis

2014 ◽  
Vol 889-890 ◽  
pp. 107-112
Author(s):  
Ji Ming Tian ◽  
Xin Tan
Keyword(s):  
Genetic Algorithm ◽  
Wind Turbine ◽  
Optimization Design ◽  
Optimal Solution ◽  
Optimization Method ◽  
Strength Analysis ◽  
Improved Genetic Algorithm ◽  
Global Optimal Solution ◽  
Wind Turbine Gearbox ◽  
Global Optimal

The design of the gearbox must ensure the simplest structure and the lightest weight under the premise of meeting the reliability and life expectancy. According to the requirement of wind turbine, an improved method combined dynamic penalty function with pseudo-parallel genetic algorithm is used to optimize gearbox. It takes the minimum volumes as object functions. It is showed that the ability to search the global optimal solution of improved genetic algorithm and less number of iterations. The global optimal solution is worked out quickly. The size parameters are optimized, as much as the driving stability and efficiency. To verify the feasibility of improved genetic algorithm, ring gear of the gearbox is analyzed. Static strength analysis shows that the optimization method is reasonable and effective.

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