Component Selection Using Genetic Algorithms

1993 ◽  
Author(s):  
Susan E. Carlson ◽  
Michael Ingrim ◽  
Ronald Shonkwiler
Keyword(s):  
Genetic Algorithms ◽  
Engineering Systems ◽  
Design Specification ◽  
Selection Problems ◽  
Component Selection ◽  
Optimal Set ◽  
System Layout

Abstract Genetic algorithms are investigated for use in obtaining optimal component configurations in dynamic engineering systems. Given a system layout, a database of component information from manufacturers’ catalogs, and a design specification, genetic algorithms are used to successfully select an optimal set of components. Five different algorithms are investigated for use on an hydraulic example. The algorithms are compared on the basis of expected hitting lime: the number of system simulations required to find the solution. One algorithm is found to have the best set of characteristics for solving component selection problems.

Selection of optimal set of weights in a layered network using genetic algorithms

1994 ◽  
Vol 80 (3-4) ◽  
pp. 213-234 ◽  
Author(s):  
Sankar K. Pal ◽  
Dinabandhu Bhandari
Keyword(s):  
Genetic Algorithms ◽  
Optimal Set ◽  
Selection Of

Comparison of Two Multiobjective Optimization Techniques With and Within Genetic Algorithms

1999 ◽  
Author(s):  
Shapour Azar ◽  
Brian J. Reynolds ◽  
Sanjay Narayanan
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Optimization Technique ◽  
Optimization Techniques ◽  
Pareto Set ◽  
Design Solution ◽  
Multiple Objective ◽  
Multiple Objective Optimization ◽  
Continuous Variables ◽  
Optimal Set

Abstract Engineering decision making involving multiple competing objectives relies on choosing a design solution from an optimal set of solutions. This optimal set of solutions, referred to as the Pareto set, represents the tradeoffs that exist between the competing objectives for different design solutions. Generation of this Pareto set is the main focus of multiple objective optimization. There are many methods to solve this type of problem. Some of these methods generate solutions that cannot be applied to problems with a combination of discrete and continuous variables. Often such solutions are obtained by an optimization technique that can only guarantee local Pareto solutions or is applied to convex problems. The main focus of this paper is to demonstrate two methods of using genetic algorithms to overcome these problems. The first method uses a genetic algorithm with some external modifications to handle multiple objective optimization, while the second method operates within the genetic algorithm with some significant internal modifications. The fact that the first method operates with the genetic algorithm and the second method within the genetic algorithm is the main difference between these two techniques. Each method has its strengths and weaknesses, and it is the objective of this paper to compare and contrast the two methods quantitatively as well as qualitatively. Two multiobjective design optimization examples are used for the purpose of this comparison.

scholarly journals Genetic Algorithms Based Reference Signal Determination for Temperature Control of Residential Buildings

2018 ◽  
Vol 8 (11) ◽  
pp. 2129 ◽  
Author(s):  
Domenico Gorni ◽  
Antonio Visioli
Keyword(s):  
Genetic Algorithms ◽  
Predictive Control ◽  
Residential Buildings ◽  
Reference Signal ◽  
Residential Building ◽  
Optimization Procedure ◽  
Software Tool ◽  
User Requirements ◽  
Optimal Set

This paper deals with the use of genetic algorithms for the determination of the optimal set-point signals for the control of the temperature in a residential building for which the use of the rooms, that is, the user requirements, are different throughout the day. In particular, the optimization procedure aims at minimizing the overall energy consumption by satisfying, at the same time, the comfort constraints set by the user. Both the case of radiators and fan-coil units are considered. The presence of unoccupied rooms is also addressed. Finally, a comparison between this approach and a Model Predictive Control based one is presented. Simulation results obtained by using TRNSYS software tool demonstrate the effectiveness of the method.

Sign in / Sign up

Export Citation Format

Share Document