Automated Search for Multidimensional Design Windows Using Evolutionary Algorithms and Clustering

Abstract

This paper proposes an automated search method for multidimensional design window (DW), which is defined as an area of satisfactory design solutions in a design parameter space, using both continuous evolutionary algorithms (CEA) and improved K-means clustering. This method consists of two phases : (a) CEA-based search for satisfactory design solutions in an ill-structured design space, and (b) clustering-based identification of DWs from the solutions. To demonstrate practical performance of the method, it is applied to an automated structural design of micro electrostatic actuator for optical memory.