Self-adaptive stepsize search for automatic optimal design

Abstract

Engineering design involves the determination of a system’s design variable values with the aim of obtaining a ‘low cost’ design that does not violate the system’s constraints. For example, in structural design, low weight (hence less material and financial cost) space trusses are required that resist specified external forces without exhibiting excessive displacement or deformation. Unfortunately, the design space is usually vast, so a computer-based approach is a natural way forward for the engineering design process. However, the use of heuristic computational optimization algorithms to automatically obtain an optimal design is usually overlooked by practitioners. This is because of the lack of a standard methodology for matching a suitable optimization algorithm with a particular design problem, and also for the need to first determine the control parameter values of the optimization algorithm prior to actually using the algorithm for design purposes. In this paper a novel population-based computational optimization algorithm, called self-adaptive stepsize search (SASS), is applied to two standard engineering design problems. Computational experiments presented in this paper demonstrate that the algorithm is very effective and also very efficient. Furthermore, it is versatile in the sense that it is not restricted to any particular application area and, importantly, it avoids the usual need to tune the algorithm parameters prior to performing the design optimization. SASS therefore provides design practitioners with a powerful and practical tool which can be used as a black-box optimizer without the need for detailed knowledge of optimization algorithms.