Genetic algorithms: concepts and applications [in engineering design]
TL;DR: In this article, the authors introduce genetic algorithms (GA) as a complete entity, in which knowledge of this emerging technology can be integrated together to form the framework of a design tool for industrial engineers.
Abstract: This paper introduces genetic algorithms (GA) as a complete entity, in which knowledge of this emerging technology can be integrated together to form the framework of a design tool for industrial engineers. An attempt has also been made to explain "why" and "when" GA should be used as an optimization tool.
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TL;DR: In this paper, a technique based on genetic algorithms is proposed for improving the accuracy of solar cell parameters extracted using conventional techniques, which is based on formulating the parameter extraction as a search and optimization problem.
Abstract: In this paper, a technique based on genetic algorithms is proposed for improving the accuracy of solar cell parameters extracted using conventional techniques. The approach is based on formulating the parameter extraction as a search and optimization problem. Current–voltage data used were generated by simulating a two-diode solar cell model of specified parameters. The genetic algorithm search range that simulates the error in the extracted parameters was varied from ± 5t o±100% of the specified parameter values. Results obtained show that for a simulated error of ±5% in the solar cell model values, the deviation of the extracted parameters varied from 0.1 to 1% of the specified values. Even with a simulated error of as high as ±100%, the resulting deviation only varied from 2 to 36%. The performance of this technique is also shown to surpass the quasi-Newton method, a calculus-based search and optimization algorithm.
285 citations
TL;DR: Computationally-tractable algebraic expressions are suggested here in contrast to the commonly-used Bouc–Wen model, which involves internal dynamics represented by a non-linear differential equation.
Abstract: Non-linear hysteresis is a complicated phenomenon associated with magnetorheological (MR) fluid dampers. A new model for MR dampers is proposed in this paper. For this, computationally-tractable algebraic expressions are suggested here in contrast to the commonly-used Bouc–Wen model, which involves internal dynamics represented by a non-linear differential equation. In addition, the model parameters can be explicitly related to the hysteretic phenomenon. To identify the model parameters, a particle swarm optimization (PSO) algorithm is employed using experimental force–velocity data obtained from various operating conditions. In our algorithm, it is possible to relax the need for a priori knowledge on the parameters and to reduce the algorithmic complexity. Here, the PSO algorithm is enhanced by introducing a termination criterion, based on the statistical hypothesis testing to guarantee a user-specified confidence level in stopping the algorithm. Parameter identification results are included to demonstrate the accuracy of the model and the effectiveness of the identification process.
282 citations
TL;DR: The improved gravitational search algorithm (IGSA), together with genetic algorithm, particle swarm optimization and GSA, is employed in parameter identification of HTGS and is shown to locate more precise parameter values than the compared methods with higher efficiency.
256 citations
01 Jul 2006
TL;DR: This paper describes the application of the genetic algorithm for the optimization of the control parameters in parallel hybrid electric vehicles (HEV) based on an electric assist control strategy (EACS) so as to minimize the vehicle engine fuel consumption and emissions.
Abstract: This paper describes the application of the genetic algorithm for the optimization of the control parameters in parallel hybrid electric vehicles (HEV). The HEV control strategy is the algorithm according to which energy is produced, used, and saved. Therefore, optimal management of the energy components is a key element for the success of a HEV. In this study, based on an electric assist control strategy (EACS), the fitness function is defined so as to minimize the vehicle engine fuel consumption (FC) and emissions. The driving performance requirements are then considered as constraints. In addition, in order to reduce the number of the decision variables, a new approach is used for the battery control parameters. Finally, the optimization process is performed over three different driving cycles including ECE-EUDC, FTP and TEH-CAR. The results from the computer simulation show the effectiveness of the approach and reduction in FC and emissions while ensuring that the vehicle performance is not sacrificed.
241 citations
TL;DR: A new, hybrid genetic algorithm (GA) technique, including hill climbing and parallel-migration, combined with a new, robust evaluation metric based on surface interpenetration is presented, which offers much faster convergence than prior GA methods and ensures more precise alignments, even in the presence of significant noise, than mean squared error or other well-known robust cost functions.
Abstract: This paper addresses the range image registration problem for views having low overlap and which may include substantial noise. The current state of the art in range image registration is best represented by the well-known iterative closest point (ICP) algorithm and numerous variations on it. Although this method is effective in many domains, it nevertheless suffers from two key limitations: it requires prealignment of the range surfaces to a reasonable starting point; and it is not robust to outliers arising either from noise or low surface overlap. This paper proposes a new approach that avoids these problems. To that end, there are two key, novel contributions in this work: a new, hybrid genetic algorithm (GA) technique, including hill climbing and parallel-migration, combined with a new, robust evaluation metric based on surface interpenetration. Up to now, interpenetration has been evaluated only qualitatively; we define the first quantitative measure for it. Because they search in a space of transformations, GA are capable of registering surfaces even when there is low overlap between them and without need for prealignment. The novel GA search algorithm we present offers much faster convergence than prior GA methods, while the new robust evaluation metric ensures more precise alignments, even in the presence of significant noise, than mean squared error or other well-known robust cost functions. The paper presents thorough experimental results to show the improvements realized by these two contributions.
234 citations
References
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01 Jan 1979
42,654 citations
Book•
01 Jan 1979
TL;DR: The second edition of a quarterly column as discussed by the authors provides a continuing update to the list of problems (NP-complete and harder) presented by M. R. Garey and myself in our book "Computers and Intractability: A Guide to the Theory of NP-Completeness,” W. H. Freeman & Co., San Francisco, 1979.
Abstract: This is the second edition of a quarterly column the purpose of which is to provide a continuing update to the list of problems (NP-complete and harder) presented by M. R. Garey and myself in our book ‘‘Computers and Intractability: A Guide to the Theory of NP-Completeness,’’ W. H. Freeman & Co., San Francisco, 1979 (hereinafter referred to as ‘‘[G&J]’’; previous columns will be referred to by their dates). A background equivalent to that provided by [G&J] is assumed. Readers having results they would like mentioned (NP-hardness, PSPACE-hardness, polynomial-time-solvability, etc.), or open problems they would like publicized, should send them to David S. Johnson, Room 2C355, Bell Laboratories, Murray Hill, NJ 07974, including details, or at least sketches, of any new proofs (full papers are preferred). In the case of unpublished results, please state explicitly that you would like the results mentioned in the column. Comments and corrections are also welcome. For more details on the nature of the column and the form of desired submissions, see the December 1981 issue of this journal.
40,020 citations
Book•
01 Jan 1975
TL;DR: Names of founding work in the area of Adaptation and modiication, which aims to mimic biological optimization, and some (Non-GA) branches of AI.
Abstract: Name of founding work in the area. Adaptation is key to survival and evolution. Evolution implicitly optimizes organisims. AI wants to mimic biological optimization { Survival of the ttest { Exploration and exploitation { Niche nding { Robust across changing environments (Mammals v. Dinos) { Self-regulation,-repair and-reproduction 2 Artiicial Inteligence Some deenitions { "Making computers do what they do in the movies" { "Making computers do what humans (currently) do best" { "Giving computers common sense; letting them make simple deci-sions" (do as I want, not what I say) { "Anything too new to be pidgeonholed" Adaptation and modiication is root of intelligence Some (Non-GA) branches of AI: { Expert Systems (Rule based deduction)
32,573 citations