This paper discusses two approaches to solve multidisciplinary optimization problems regarding complex engineering systems. These approaches have been developed in order to reduce computing time expenditures required for solution of such problems. The first approach is based on utilization of parallel computations not only for object function computing, but also for an "internal" operation of the algorithm that is parallelized. This allows for a significantly higher acceleration of the problem solution process then a trivial usage of parallel CPUs for optimization criteria calculation. The results of numerical testing for the new algorithm are presented. The second approach consists of using multiple fidelity (multilevel) analysis algorithms. The results of a real-life stochastic multiobjective optimization problem solution are presented. The usage of multilevel approach for such optimization problems results in a significant reduction of computing time.
[1]
I. N. Egorov,et al.
Optimization of Gas Turbine Engine Elements by Probability Criteria
,
1993
.
[2]
I. N. Egorov,et al.
Search for compromise solution of the multistage axial compressor’s stochastic optimization problem
,
1998
.
[3]
Gene H. Golub,et al.
Scientific computing: an introduction with parallel computing
,
1993
.
[4]
I. N. Egorov,et al.
Optimization of a Multistage Axial Compressor Stochastic Approach
,
1992
.
[5]
I. N. Egorov,et al.
The Methodology of Stochastic Optimization of Parameters and Control Laws for the Aircraft Gas-Turbine Engines Flow Passage Components
,
1999
.