Abstract
This paper developed a new method that adaptively adjusts a design space by considering the actual solution distribution of a problem to overcome the conventional design-space adaptation method that assumes the solutions distribution to be a normal distribution because the distributions of solutions are rarely normal distributions for real-world problems. The developed method was applied to nineteen multiobjective test functions that are widely used to evaluate the characteristics and performance of optimization approaches. The results showed that this method adapted the design space to an appropriate design space where the solution existence probability was high. The optimization performance achieved using the developed method was higher than that of the conventional methods. Furthermore, the developed method was applied to the conceptual design of an unmanned spacecraft to confirm its validity in real-world design and multidisciplinary-optimization problems. The results showed that the Pareto solutions of the developed method were superior to those of conventional methods. Additionally, the optimization efficiency with the developed method was improved by more than 1.4 times over that of the conventional methods. In this regard, the developed method has the potential to be applied to complicated real-world optimization problems to achieve better performance and efficiency.