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CivilComp Proceedings
ISSN 17593433 CCP: 80
PROCEEDINGS OF THE FOURTH INTERNATIONAL CONFERENCE ON ENGINEERING COMPUTATIONAL TECHNOLOGY Edited by: B.H.V. Topping and C.A. Mota Soares
Paper 105
Neural Networks and Evolutionary Algorithms for solving Multi Objective Optimisation Problems B. Ait Brik, N. Bouhaddi and S. Cogan
Applied Mechanics Laboratory R. Chaléat, UMR 6604 CNRS, University of Franche Comté, Besançon, France B. Ait Brik, N. Bouhaddi, S. Cogan, "Neural Networks and Evolutionary Algorithms for solving Multi Objective Optimisation Problems", in B.H.V. Topping, C.A. Mota Soares, (Editors), "Proceedings of the Fourth International Conference on Engineering Computational Technology", CivilComp Press, Stirlingshire, UK, Paper 105, 2004. doi:10.4203/ccp.80.105
Keywords: multiobjective optimization, evolutionary algorithms, neural networks, uncertainties, robustness, self organizing map.
Summary
In the field of mechanical engineering, much effort has been devoted to multiobjective
evolutionary algorithms (MOEA) [1,2] since they provide a unique opportunity to address
global tradeoffs between multiple objective functions by sampling a number of Pareto
solutions
In engineering, uncertainties are a result of defects in materials properties (Young modulus, density, ...), and manufacturing processes (thickness, other geometrical variables, ...). In the preliminary design phase, these uncertainties are introduced to take into account the much of knowledge inherent in certain design variables. The deterministic approaches of optimization neglect the effects of uncertainty in the design space; solutions may violate the physical reality. To mitigate this difficulty, we proposed in this paper a stochastic optimization method. The goal of proposed method is to find the robust solutions by introducing additional cost functions (known as the robustness functions) for each original cost functions. This robustness function is defined to have the dispersion of the original cost function. The robustness functions and original functions are evaluated simultaneously. A Monte Carlo sampling is used to take into account uncertainties on the design parameters. This is performed by using an evolutionary multiobjective optimization in order to find all Pareto optimal solutions. To reduce considerably the computing time, we used a neural network in order to approximate the cost functions and to evaluate the robustness functions. In this case, A multilayer perceptron is used and a back propagation algorithm is employed to train the network [3]. Neural networks can be used to simulate multiinput, multioutput systems. They have been used as fast structural analysis and design tools. An advantage of neural networks is that they can simulate very complex, highly nonlinear systems problems. The development of a neural network consists of the following steps:
The optimal solutions resulting from the stochastic optimization must be analyzed, only a few solutions were examined in detail. This is a typical case that computer produces/accumulates too much data, datamining techniques are needed. One of popular datamining techniques is the Self Organizing Map (SOM) by Kohonen [4]. The SOM is one of the neural network models. The SOM algorithm is based on unsupervised, competitive learning. It provides a topology preserving mapping from the high dimensional space to map, usually from a twodimensional lattice and thus the mapping is a mapping from high dimensional space onto a plane. The property of topology preserving means that the mapping preserves the relative distance between the points, and each points are near other in the input space are mapped to nearby map units in the SOM. The SOM can thus serve as a cluster analyzing tool of highdimensional data. In this paper, the SOM is applied to map Pareto solutions obtained by the stochastic optimization. This will reveal the global tradeoffs between objective functions. Furthermore, from clusters obtained in the SOM, the relations between design variables are mapped onto another SOM. This will indicate the relative importance of design variables and their interactions. The proposed examples illustrate the interest of suggested methodology to solve stochastic multi objective optimisation problems in structural dynamics References
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