Evolving Hidden Genes in Genetic Algorithms for Systems Architecture Optimization

2018 ◽  
Vol 140 (10) ◽  
Author(s):  
Ossama Abdelkhalik ◽  
Shadi Darani
Keyword(s):  
Genetic Algorithms ◽  
Trajectory Optimization ◽  
Optimization Problems ◽  
Mathematical Optimization ◽  
Space Mission ◽  
Systems Architecture ◽  
Design Variables ◽  
Interplanetary Trajectory ◽  
Second Category ◽  
Architecture Optimization

The concept of hidden genes was recently introduced in genetic algorithms (GAs) to handle systems architecture optimization problems, where the number of design variables is variable. Selecting the hidden genes in a chromosome determines the architecture of the solution. This paper presents two categories of mechanisms for selecting (assigning) the hidden genes in the chromosomes of GAs. These mechanisms dictate how the chromosome evolves in the presence of hidden genes. In the proposed mechanisms, a tag is assigned for each gene; this tag determines whether the gene is hidden or not. In the first category of mechanisms, the tags evolve using stochastic operations. Eight different variations in this category are proposed and compared through numerical testing. The second category introduces logical operations for tags evolution. Both categories are tested on the problem of interplanetary trajectory optimization for a space mission to Jupiter, as well as on mathematical optimization problems. Several numerical experiments were designed and conducted to optimize the selection of the hidden genes algorithm parameters. The numerical results presented in this paper demonstrate that the proposed concept of tags and the assignment mechanisms enable the hidden genes genetic algorithms (HGGA) to find better solutions.

scholarly journals Autonomous Planning of Multigravity-Assist Trajectories with Deep Space Maneuvers Using a Differential Evolution Approach

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Ossama Abdelkhalik
Keyword(s):  
Genetic Algorithms ◽  
Differential Evolution ◽  
Optimization Problems ◽  
Optimal Number ◽  
Deep Space ◽  
Biologically Inspired ◽  
Autonomous Planning ◽  
Design Variables ◽  
Planning Problems ◽  
Evolution Approach

The biologically inspired concept of hidden genes has been recently introduced in genetic algorithms to solve optimization problems where the number of design variables is variable. In multigravity-assist trajectories, the hidden genes genetic algorithms demonstrated success in searching for the optimal number of swing-bys and the optimal number of deep space maneuvers. Previous investigations in the literature for multigravity-assist trajectory planning problems show that the standard differential evolution is more effective than the standard genetic algorithms. This paper extends the concept of hidden genes to differential evolution. The hidden genes differential evolution is implemented in optimizing multigravity-assist space trajectories. Case studies are conducted, and comparisons to the hidden genes genetic algorithms are presented in this paper.

Development of a Changeable Airfoil Optimization Model for Use in the Multidisciplinary Design of Unmanned Aerial Vehicles

2009 ◽  
Author(s):  
Scott Ferguson ◽  
Andrew H. Tilstra ◽  
Carolyn C. Seepersad ◽  
Kristin L. Wood
Keyword(s):  
Optimization Problem ◽  
Design Research ◽  
Optimization Problems ◽  
Mathematical Optimization ◽  
Operating Conditions ◽  
Multidisciplinary Optimization ◽  
System Level ◽  
Airfoil Optimization ◽  
Design Variables ◽  
Subsystem Design

Complex systems need to perform in a variety of functional states and under varying operating conditions. Therefore, it is important to manage the different values of design variables associated with the operating states for each subsystem. The research presented in this paper uses multidisciplinary optimization (MDO) and changeable systems methods together in the design of a reconfigurable Unmanned Aerial Vehicle (UAV). MDO is a useful approach for designing a system that is composed of distinct disciplinary subsystems by managing the design variable coupling between the subsystem and system level optimization problems. Changeable design research addresses how changes in the physical configuration of products and systems can better meet distinct needs of different operating states. As a step towards the development of a realistic reconfigurable UAV optimization problem, this paper focuses on the performance advantage of using a changeable airfoil subsystem. Design principles from transformational design methods are used to develop concepts that determine how the design variables are allowed to change in the mathematical optimization problem. The performance of two changeable airfoil concepts is compared to a fixed airfoil design over two different missions that are defined by a sequence of mission segments. Determining the configurations of the static and changeable airfoils is accomplished using a genetic algorithm. Results from this study show that aircraft with changeable airfoils attain increased performance, and that the manner by which the system transforms is significant. For this reason, the changeable airfoil optimization developed in this paper is ready to be integrated into a complete MDO problem for the design of a reconfigurable UAV.

scholarly journals Unconstrained numerical optimization using real-coded genetic algorithms: a study case using benchmark functions in R from Scratch

2019 ◽  
Vol 11 (3) ◽  
pp. 1-11
Author(s):  
Omar Andres Carmona Cortes ◽  
Josenildo Costa da Silva
Keyword(s):  
Genetic Algorithms ◽  
Numerical Optimization ◽  
Optimization Problems ◽  
Practical Applications ◽  
R Language ◽  
Mutation Operators ◽  
Design Variables ◽  
Study Case ◽  
Selection Mechanisms ◽  
Real Coded Genetic Algorithms

Unconstrained numerical problems are common in solving practical applications that, due to its nature, are usually devised by several design variables, narrowing the kind of technique or algorithm that can deal with them. An interesting way of tackling this kind of issue is to use an evolutionary algorithm named Genetic Algorithm. In this context, this work is a tutorial on using real-coded genetic algorithms for solving unconstrained numerical optimization problems. We present the theory and the implementation in R language. Five benchmarks functions (Rosenbrock, Griewank, Ackley, Schwefel, and Alpine) are used as a study case. Further, four different crossover operators (simple, arithmetical, non-uniform arithmetical, and Linear), two selection mechanisms (roulette wheel and tournament), and two mutation operators (uniform and non-uniform) are shown. Results indicate that non-uniform mutation and tournament selection tend to present better outcomes.

Solving marketing optimization problems using genetic algorithms

1995 ◽  
Vol 29 (4) ◽  
pp. 39-56 ◽  
Author(s):  
S. Hurley ◽  
L. Moutinho ◽  
N.M. Stephens
Keyword(s):  
Genetic Algorithms ◽  
Optimization Problems

Implementation of Eurocode Load Cases in Optimization Problems of Steel Frames, Based on Genetic Algorithms

2013 ◽  
Vol 310 ◽  
pp. 609-613
Author(s):  
Ioana D. Balea ◽  
Radu Hulea ◽  
Georgios E. Stavroulakis
Keyword(s):  
Finite Element ◽  
Genetic Algorithms ◽  
Global Optimization ◽  
Optimal Design ◽  
Optimization Problems ◽  
Steel Frames ◽  
Global Optimization Algorithm ◽  
Planar Structures ◽  
Discrete Global Optimization ◽  
Steel Sections

This paper presents an implementation of Eurocode load cases for discrete global optimization algorithm for planar structures based on the principles of finite element methods and genetic algorithms. The final optimal design is obtained using IPE sections chosen as feasible by the algorithm, from the available steel sections from industry. The algorithm is tested on an asymmetric planar steel frame with promising results.

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