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Multilevel Optimization Algorithms Based on Metamodel- and Fitness Inheritance-Assisted Evolutionary Algorithms

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Computational Intelligence in Expensive Optimization Problems

Part of the book series: Adaptation Learning and Optimization ((ALO,volume 2))

Abstract

This chapter is concerned with the efficient use of metamodel-assisted evolutionary algorithms built in multilevel or hierarchical schemes for the solution of computationally expensive optimization problems. Existing methods developed by other researchers or the authors’ group are overviewed and a new enhancement based on fitness inheritance is proposed. Whereas conventional evolutionary algorithms require a great number of calls to the evaluation software, the use of low cost surrogates or metamodels, trained on the fly on previously evaluated individuals for pre-evaluating the evolving populations, reduce noticeably the CPU cost of an optimization. Since, themetamodel training requires a minimum amount of previous evaluations, the starting population is evaluated on the problem-specific model. Fitness inheritance is introduced in this context so as to approximate the objective function values in place of metamodels. In addition, to profit of the availability of evaluation or parameterization models of lower fidelity and CPU cost and/or refinement methods, a multilevel search algorithm relying also on the use of metamodels is presented. The algorithm may optionally operate as hierarchical-distributed (many levels performing distributed optimization) or distributed-hierarchical (more than one sub-populations undergoing their own hierarchical optimizations) to further reduce the design cycle time. The proposed algorithms are generic and can be used to solve any kind of optimization problems. Here, aerodynamic shape optimization problems, including turbomachinery applications, are used to demonstrate the efficiency of the proposed methods. A new computationally demanding application, namely the optimization of a 3D compressor blade is also shown.

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Authors and Affiliations

  1. School of Mechanical Engineering, Lab. of Thermal Turbomachines, Parallel CFD & Optimization Unit, National Technical University of Athens, P.O. Box 64069, Athens, 157 10, Greece

    Kyriakos C. Giannakoglou & Ioannis C. Kampolis

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  1. Kyriakos C. Giannakoglou
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  2. Ioannis C. Kampolis
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  1. Department of Mechanical Engineering and Science-Faculty of Engineering, Kyoto University, Yoshida-honmachi, Sakyo-Ku,, 606-8501, Kyoto, Japan

    Yoel Tenne

  2. Advanced Technology Centre, Rolls-Royce Singapore Pte Ltd, 50 Nanyang Avenue, Block N2, Level B3C,Unit 05-08, 639798, Singapore

    Chi-Keong Goh

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Giannakoglou, K.C., Kampolis, I.C. (2010). Multilevel Optimization Algorithms Based on Metamodel- and Fitness Inheritance-Assisted Evolutionary Algorithms. In: Tenne, Y., Goh, CK. (eds) Computational Intelligence in Expensive Optimization Problems. Adaptation Learning and Optimization, vol 2. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10701-6_3

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