Evolution strategies for engineering design optimisation

Abstract

Computer simulations of complex engineering problems have become a standard tool of modern product development and design. The increasing computational power at modest costs leads to a growing interest in directly using computer simulation codes for automatic product optimization. Traditional numerical optimization methods have some drawbacks that make them difficult to use with complex simulation software. Gradient-based methods are always local optimizers, thus requiring additional methods such as random restarts to find global optima. Evolutionary optimization is a way to overcome some of these limitations. This chapter presents a paper that introduces evolution strategies as a robust and fault-tolerant optimization method, which does not rely on gradients, is easily adaptable to massively parallel computing systems and can be used for single and multiple-criteria optimization. It describes a complex and aerodynamical test problem that was solved by an evolution strategy. This paper introduces the basic elements of evolution strategies and addresses their important features such as self adaptation, robustness, and multiprocessor implementations.