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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.