Practical Design Optimization Techniques

Genetic algorithms are an attractive choice for researchers and practitionersof design optimization. Their advantages are many as compared to classicaloptimization approaches. However, there are a number of areas to look intoso as to make them more practically relevant. This thesis describes originalcontributions to three such areas Robust Design Optimization, ConstraintHandling and Co-Kriging.Robust design optimization using genetic algorithms is computationally expensive because of the high number of function evaluations involved. Hence, it is often simply not possible to use such procedures in practice. We propose some enhancements to an existing robust design optimization algorithm by making it less resource intensive and hence more practically applicable. We study a number of Bi-objective Engineering design optimization problems to understand and demonstrate the benefits of the proposed enhancements. A highlight of this study is the robust design optimization study of an Axisymmetric Scramjet configuration. Next, an interesting and novel constraint handling technique based on the so-called Most probable point (of failure) is described. The proposed method is applicable to optimization problems with equality and active inequality constraints. We present the findings of the proposed approach by considering a test bed of a number of Single and Multi-objective optimization problems. The test bed consists of standard synthetic test problems and engineering design optimization problems. A key contribution here is theformulation of a number of Multi-objective equality constrained test problems.Finally, an interesting modification to Co-Kriging is proposed. Co-Kriging seeks to combine training data obtained at multiple levels of fidelity to enhance the performance of Kriging. It however introduces a number of additional hyper-parameters as compared to Kriging. Determining their optimal values is critical to obtaining a successful Co-Kriging model. A genetic algorithm based optimization technique to find the values of these hyper-paramaters is proposed. The proposed technique also does away withthe need to evaluate the likelihood functions and hence dealing with large data sets become more tractable. A number of mathematical examples demonstrate the application of the proposed technique. As a more practical example, data sets obtained from a Scramjet design optimization exercise is considered.The results presented as part of each of the above areas will clearly demonstrate the potential of the proposed techniques in making the use of genetic algorithms in design optimization more practical and viable. The findings also helped us in coming up with a number of avenues for future work in these directions which concludes this thesis