scholarly journals Adaptation by Nash Games in Gradient-Based Multi-objective/Multi-disciplinary Optimization

2021 ◽  
pp. 107-133
Author(s):  
J.-A. Désidéri
Keyword(s):  
Nash Games ◽  
Multi Objective ◽  
Gradient Based

EVOLUTIONARY MULTI-OBJECTIVE OPTIMISATION OF NEURAL NETWORKS FOR FACE DETECTION

2004 ◽  
Vol 04 (03) ◽  
pp. 237-253 ◽  
Author(s):  
STEFAN WIEGAND ◽  
CHRISTIAN IGEL ◽  
UWE HANDMANN
Keyword(s):  
Search Strategy ◽  
Human Face ◽  
Video Streams ◽  
Feed Forward Neural Network ◽  
Image Region ◽  
Multi Objective ◽  
Strategy Adaptation ◽  
Gradient Based ◽  
Speed And Accuracy ◽  
Single Objective

For face recognition from video streams speed and accuracy are vital aspects. The first decision whether a preprocessed image region represents a human face or not is often made by a feed-forward neural network (NN), e.g. in the Viisage-FaceFINDER® video surveillance system. We describe the optimisation of such a NN by a hybrid algorithm combining evolutionary multi-objective optimisation (EMO) and gradient-based learning. The evolved solutions perform considerably faster than an expert-designed architecture without loss of accuracy. We compare an EMO and a single objective approach, both with online search strategy adaptation. It turns out that EMO is preferable to the single objective approach in several respects.

A New Deterministic Approach Using Sensitivity Region Measures for Multi-Objective Robust and Feasibility Robust Design Optimization

2005 ◽  
Vol 128 (4) ◽  
pp. 874-883 ◽  
Author(s):  
Mian Li ◽  
Shapour Azarm ◽  
Art Boyars
Keyword(s):  
Pareto Front ◽  
Optimization Problems ◽  
Continuous Functions ◽  
Robust Design Optimization ◽  
Worst Case ◽  
New Approach ◽  
Multi Objective ◽  
Tolerance Region ◽  
Parameter Variations ◽  
Gradient Based

We present a deterministic non-gradient based approach that uses robustness measures in multi-objective optimization problems where uncontrollable parameter variations cause variation in the objective and constraint values. The approach is applicable for cases that have discontinuous objective and constraint functions with respect to uncontrollable parameters, and can be used for objective or feasibility robust optimization, or both together. In our approach, the known parameter tolerance region maps into sensitivity regions in the objective and constraint spaces. The robustness measures are indices calculated, using an optimizer, from the sizes of the acceptable objective and constraint variation regions and from worst-case estimates of the sensitivity regions’ sizes, resulting in an outer-inner structure. Two examples provide comparisons of the new approach with a similar published approach that is applicable only with continuous functions. Both approaches work well with continuous functions. For discontinuous functions the new approach gives solutions near the nominal Pareto front; the earlier approach does not.

scholarly journals Single and multi–objective UAV aerofoil optimisation via hierarchical asynchronous parallel evolutionary algorithm

2006 ◽  
Vol 110 (1112) ◽  
pp. 659-672 ◽  
Author(s):  
L. F. Gonzalez ◽  
D. S. Lee ◽  
K. Srinivas ◽  
K. C. Wong
Keyword(s):  
Traditional Approach ◽  
Global Solutions ◽  
Shape Optimisation ◽  
Test Cases ◽  
Navigation Systems ◽  
Local Optima ◽  
Multi Objective ◽  
Gradient Based ◽  
Parallel Evolutionary Algorithm ◽  
Operational Envelope

Abstract Unmanned aerial vehicle (UAV) design tends to focus on sensors, payload and navigation systems, as these are the most expensive components. One area that is often overlooked in UAV design is airframe and aerodynamic shape optimisation. As for manned aircraft, optimisation is important in order to extend the operational envelope and efficiency of these vehicles. A traditional approach to optimisation is to use gradient-based techniques. These techniques are effective when applied to specific problems and within a specified range. These methods are efficient for finding optimal global solutions if the objective functions and constraints are differentiable. If a broader application of the optimiser is desired, or when the complexity of the problem arises because it is multi-modal, involves approximation, is non-differentiable, or involves multiple objectives and physics, as it is often the case in aerodynamic optimisation, more robust and alternative numerical tools are required. Emerging techniques such as evolutionary algorithms (EAs) have been shown to be robust as they require no derivatives or gradients of the objective function, have the capability of finding globally optimum solutions among many local optima, are easily executed in parallel, and can be adapted to arbitrary solver codes without major modifications. In this paper, the formulation and application of a evolutionary technique for aerofoil shape optimisation is described. Initially, the paper presents an introduction to the features of the method and a short discussion on multi-objective optimisation. The method is first illustrated on its application to mathematical test cases. Then it is applied to representative test cases related to aerofoil design. Results indicate the ability of the method for finding optimal solutions and capturing Pareto optimal fronts.

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