Affiliation:
1. Department of Mechanical Engineering, University of Maryland, College Park, MD 20742
Abstract
The high computational cost of population based optimization methods, such as multi-objective genetic algorithms (MOGAs), has been preventing applications of these methods to realistic engineering design problems. The main challenge is to devise methods that can significantly reduce the number of simulation (objective∕constraint functions) calls. We present a new multi-objective design optimization approach in which the Kriging-based metamodeling is embedded within a MOGA. The proposed approach is called Kriging assisted MOGA, or K-MOGA. The key difference between K-MOGA and a conventional MOGA is that in K-MOGA some of the design points are evaluated on-line using Kriging metamodeling instead of the actual simulation model. The decision as to whether the simulation or its Kriging metamodel should be used for evaluating a design point is based on a simple and objective criterion. It is determined whether by using the objective∕constraint functions’ Kriging metamodels for a design point, its “domination status” in the current generation can be changed. Seven numerical and engineering examples with different degrees of difficulty are used to illustrate applicability of the proposed K-MOGA. The results show that on the average K-MOGA converges to the Pareto frontier with an approximately 50% fewer number of simulation calls compared to a conventional MOGA.
Subject
Computer Graphics and Computer-Aided Design,Computer Science Applications,Mechanical Engineering,Mechanics of Materials
Reference31 articles.
1. Farina, M.
, 2001, “A Minimal Cost Hybrid Strategy for Pareto Optimal Front Approximation,” Evolutionary Optimization, an international journal on the internet, 3(1), pp. 41–52 (available online at www.jeo.org).
2. A Neural Network Based Generalized Response Surface Multiobjective Evolutionary Algorithm;Farina
3. Optimization of Large-Scale 3D Trusses Using Evolution Strategies and Neural Networks;Papadrakakis;Int. J. Space Struct.
4. Acceleration of the Convergence Speed of Evolutionary Algorithms Using Multilayer Neural Networks;Hong;Eng. Optimiz.
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