An Adaptive Real-Coded Genetic Algorithm for Size and Shape Optimization of Truss Structures

2009 ◽  
Author(s):  
K. Koohestani ◽  
S. Kazemzadeh Azad
Keyword(s):  
Genetic Algorithm ◽  
Shape Optimization ◽  
Truss Structures ◽  
Size And Shape ◽  
Real Coded Genetic Algorithm

A Novel Combination of Adaptive Tools for Turbomachinery Airfoil Shape Optimization Using a Real-Coded Genetic Algorithm

2013 ◽  
Author(s):  
A. Safari ◽  
H. G. Lemu ◽  
M. Assadi
Keyword(s):  
Genetic Algorithm ◽  
Shape Optimization ◽  
Performance Enhancement ◽  
Flow Analysis ◽  
Three Dimensional ◽  
Pressure Rise ◽  
Computation Time ◽  
Point Of View ◽  
Compressor Rotor ◽  
Real Coded Genetic Algorithm

An automated shape optimization methodology for a typical heavy-duty gas turbine (GT) compressor rotor blade section is presented in this paper. The approach combines a Non-Uniform Rational B-Spline (NURBS) driven parametric geometry description, a two-dimensional flow analysis, and a Genetic Algorithm (GA)-based optimization route. The objective is minimizing the total pressure losses for design condition as well as maximizing the airfoils operating range which is an assessment of the off-design behavior. To achieve the goal, design optimization process is carried out by coupling an established MATLAB code for the Differential Evolution (DE)-based optimum parameterized curve fitting of the measured point cloud of the airfoils’ shape, a blade-to-blade flow analysis in COMSOL Multiphysics, and a developed real-coded GA in MATLAB script. Using the combination of these adaptive tools and methods, the first results are considerably promising in terms of computation time, ability to extend the methodology for three-dimensional and multidisciplinary approach, and last but not least airfoil shape performance enhancement from efficiency and pressure rise point of view.

scholarly journals Discrete Size and Shape Optimization of Truss Structures Based on Job Search Inspired Strategy and Genetic Operations

2020 ◽  
Author(s):  
Igor Serpik
Keyword(s):  
Shape Optimization ◽  
Objective Function ◽  
Job Search ◽  
Structure Type ◽  
Truss Structures ◽  
Genetic Operators ◽  
Load Bearing Capacity ◽  
Size And Shape ◽  
Solution Accuracy ◽  
Initial Objective

A meta-heuristic algorithm for discrete size and shape optimization of trusses via a job search inspired strategy together with genetic operators of mutation, selection, and crossover is proposed. The alternation of movements with respect to objective function and load bearing capacity of constructive decisions is provided. Being introduced is an intermediate search goal connected in terms of posed limitations with heightened suitability levels of individuals meeting the current requirements for the initial objective function. As soon as these conditions allow achieving a structure type which meets task limitations, requirements for the function value are redefined. This technique does not demand penalty functions that provide strict control of limitations in any algorithm usage, greater stability of the results received, and finding better solutions. The efficiency of this approach in terms of solution accuracy is demonstrated through five benchmark design examples, in comparison with other methods of discrete truss structure optimization.

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