Multi-Objective Evolutionary Algorithm for Optimization of Combustion Processes

Büche, Dirk; Stoll, Peter; Koumoutsakos, Petros

doi:10.1007/978-3-7091-2792-6_12

Cited by 8 publications

(18 citation statements)

References 5 publications

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“…This noise can be seen as additional values randomly added to or subtracted from the real fitness value. Since the noisy fitness value is used for selection, it can mislead the algorithm to inferior results; bad solutions might be kept for the next generation, and the good ones might be excluded [1]. Formally, a noisy fitness function takes the following form:…”

Section: Noisementioning

confidence: 99%

“…The source of noise comes in any form such as normal distribution or uniform distribution. In general, the normal distribution is often used to simulate noise [1]. In the context of EMOs, there are a few techniques to deal with noise that have been introduced to date.…”

Section: Noisementioning

confidence: 99%

“…Evolutionary algorithms (EAs), particulary genetic algorithms (GA), are known to be robust in the presence of noise [1,5]. Population based methods are generally known to be robust in the single objective case against noise since the average performance of the population acts as a filter for noise.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fitness inheritance for noisy evolutionary multi-objective optimization

Bui

Abbass

Essam

2005

Proceedings of the 7th Annual Conference on Genetic and Evolutionary Computation

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This paper compares the performance of anti-noise methods, particularly probabilistic and re-sampling methods, using NSGA2. It then proposes a computationally less expensive approach to counteracting noise using re-sampling and fitness inheritance. Six problems with different difficulties are used to test the methods. The results indicate that the probabilistic approach has better convergence to the Pareto optimal front, but it looses diversity quickly. However, methods based on re-sampling are more robust against noise but they are computationally very expensive to use. The proposed fitness inheritance approach is very competitive to re-sampling methods with much lower computational cost.

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Section: Noisementioning

confidence: 99%

Section: Noisementioning

confidence: 99%

See 1 more Smart Citation

Fitness inheritance for noisy evolutionary multi-objective optimization

Bui

Abbass

Essam

2005

Proceedings of the 7th Annual Conference on Genetic and Evolutionary Computation

View full text Add to dashboard Cite

show abstract

“…Generally speaking, evolutionary algorithms (EAs) are known to be robust in the presence of noise (Hughes, 2001;Buche et al, 2002;Beyer, 2000). Population-based methods are known to be robust in the single objective case against noise since the average performance of the population acts as a filter for noise.…”

Section: Introductionmentioning

confidence: 99%

Localization for Solving Noisy Multi-Objective Optimization Problems

Bui

Abbass

Essam

2009

Evolutionary Computation

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This paper investigates the use of a framework of local models in the context of noisy evolutionary multi-objective optimization. Within this framework, the search space is explicitly divided into several nonoverlapping hyperspheres. A direction of improvement, which is related to the average performance of the spheres, is used for moving solutions within each sphere. This helps the local models to filter noise and increase the robustness of the evolutionary algorithm in the presence of noise. A wide range of noisy problems we used for testing and the experimental results demonstrate the ability of local models to better filter noise in comparison with that of global models.

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“…[26][27][28][29] For that type of optimization problems, access to the optimal solutions is of greatest interest to the designer. However, it should not prevent from identifying the tendencies and depen-dencies of the design to critical parameters which are valuable information for future developments.…”

mentioning

confidence: 99%

Computational-Fluid-Dynamics-Based Kriging Optimization Tool for Aeronautical Combustion Chambers

2009

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Current state-of-the-art in Computational Fluid Dynamics (CFD) provides reasonable reacting flow predictions and is already used in industry to evaluate new concepts of gas turbine engines. In parallel, optimization techniques have reached maturity and several industrial activities benefit from enhanced search algorithms.However, coupling a physical model with an optimization algorithm to yield a decision making tool, needs to be undertaken with care to take advantage of the current computing power while satisfying the gas turbine industrial constraints. Among the many delicate issues for such tools to contribute efficiently to the gas turbine industry, combustion is probably the most challenging and optimization algorithms are not easily applicable to such problems. In our study, a fully encapsulated algorithm addresses the issue by making use of a new multi-objective optimization strategy based on an iteratively enhanced meta-model (Kriging) coupled to a Design of Experiments (DoE) method and a fully parallel three dimensional (3D) CFD solver to model turbulent reacting flows. With this approach, the computer cost needed for thousands of CFD computations is greatly reduced while ensuring an automatic error reduction of the approximated response function. Preliminary assessments of the search algorithm against simple analytical test functions prove the strategy to be efficient and robust. Application to a 3D industrial aeronautical combustion chamber demonstrates the approach to be feasible with currently available computing power. One result of the optimization is that possible design changes can improve performance and durability of the studied engine. With the advent of * Post. of 43American Institute of Aeronautics and Astronautics massively parallel architectures, the intersection between these two advanced techniques seems a logical path to yield fully automated decision making tools for the design of gas turbine engines.

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Multi-Objective Evolutionary Algorithm for Optimization of Combustion Processes

Cited by 8 publications

References 5 publications

Fitness inheritance for noisy evolutionary multi-objective optimization

Fitness inheritance for noisy evolutionary multi-objective optimization

Localization for Solving Noisy Multi-Objective Optimization Problems

Computational-Fluid-Dynamics-Based Kriging Optimization Tool for Aeronautical Combustion Chambers

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