Evolving Robust Solutions in Multi-Objective Optimization

Abstract

As described in Chapter 7, variations in design variables or the environment may affect solution quality and design performance adversely and robust optimization considers the effects explicitly and seeks to minimize the consequences without eliminating efficiency. Many different approaches, including Taguchi orthogonal arrays, response surface methodology, probabilistic design analysis, have been applied for robust optimization. In operational research, robust optimization is considered as a modeling methodology where robust problems are reformulated into the form of linear, conic quadratic, and semi-definite programming problems. Nonetheless, assumptions or approximations are often made during problem reformulation to ensure computational tractability, resulting in more uncertainties in the problem model. In addition, it does not allow for the incorporation of any domain knowledge to achieve better performance. On the other hand, evolutionary optimization techniques do not have such limitations, making it appropriate for robust optimization.