Journal Article10.1016/J.ASOC.2007.02.004
Multiobjective optimization using variable complexity modelling for control system design
Valceres Vieira Rocha e Silva,Peter J. Fleming,Jungiro Sugimoto,Ryuichi Yokoyama +3 more
- 01 Jan 2008
- Vol. 8, Iss: 1, pp 392-401
28
TL;DR: A multi-stage design approach that uses a multiobjective genetic algorithm as the framework for optimization and multiObjective preference articulation, and an H_infty loop-shaping technique are used to design controllers for a gas turbine engine.
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Abstract: A multi-stage design approach that uses a multiobjective genetic algorithm as the framework for optimization and multiobjective preference articulation, and an H_infty loop-shaping technique are used to design controllers for a gas turbine engine. A non-linear model is used to assess performance of the controller. Because the computational load of applying multiobjective genetic algorithm to this control strategy is very high, a neural network and response surface models are used in order to speed up the design process within the framework of a multiobjective genetic algorithm. The final designs are checked using the original non-linear model.
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Citations
Multiobjective evolutionary algorithms: A survey of the state of the art
TL;DR: This paper surveys the development ofMOEAs primarily during the last eight years and covers algorithmic frameworks such as decomposition-based MOEAs (MOEA/Ds), memetic MOEas, coevolutionary MOE As, selection and offspring reproduction operators, MOE as with specific search methods, MOeAs for multimodal problems, constraint handling and MOE
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Controller Tuning by Means of Multi-Objective Optimization Algorithms: A Global Tuning Framework
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Predicting patient survival after liver transplantation using evolutionary multi-objective artificial neural networks
M. Cruz-Ramírez,César Hervás-Martínez,Juan Carlos Fernández,Javier Briceño,Manuel de la Mata +4 more
TL;DR: The proposed rule-based system minimises the prediction probability error produced by two sets of models such that it maximises the probability of success in liver transplants, with success based on graft survival three months post-transplant.
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Multiobjective optimization of injection molding parameters based on soft computing and variable complexity method
TL;DR: In this article, a methodology integrating variable complexity methods (VCMs), constrained non-dominated sorted genetic algorithm (CNSGA), back propagation neural networks (BPNNs) and Moldflow analyses was put forward to locate the Pareto optimal solutions to the constrained multiobjective optimization problem.
68
References
Genetic algorithms in search, optimization and machine learning
David E. Goldberg
- 01 Jan 1989
TL;DR: This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.
58.6K
•Book
Genetic algorithms in search, optimization, and machine learning
David E. Goldberg
- 01 Sep 1988
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
•Proceedings Article
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Carlos M. Fonseca,Peter J. Fleming +1 more
- 01 Jun 1993
TL;DR: A rank-based fitness assignment method for Multiple Objective Genetic Algorithms (MOGAs) and the genetic algorithm is seen as the optimizing element of a multiobjective optimization loop, which also comprises the DM.
3.5K
An overview of evolutionary algorithms in multiobjective optimization
TL;DR: Current multiobjective evolutionary approaches are discussed, ranging from the conventional analytical aggregation of the different objectives into a single function to a number of population-based approaches and the more recent ranking schemes based on the definition of Pareto optimality.
•Book
Robust Controller Design Using Normalized Coprime Factor Plant Descriptions
Keith Glover,Duncan McFarlane +1 more
- 11 Dec 1989
TL;DR: A load dumping vehicle including a frame, a gas turbine engine supported by the frame and a dump body pivotally connected to theframe for movement relative to the frame between a load carrying position and adump position.
1.1K