Hybrid Single and Multiobjective Optimization for Engineering Design without Exact Specifications

Liu, B. , Akinsolu, M. O. and Zhang, Q. (2020) Hybrid Single and Multiobjective Optimization for Engineering Design without Exact Specifications. In: IEEE WCCI 2020, Glasgow, UK, 19-24 July 2020, ISBN 9781728169293 (doi: 10.1109/CEC48606.2020.9185804)

Abstract

A challenge in engineering design optimization is that sufficient information may not be available to define the exact specifications beforehand. While iterative trial optimization using different specifications is widely used in industry, multiobjective optimization is attracting much attention in the academic field. However, off-the-shelf methods in both categories are time-consuming due to the involved computationally expensive simulations. In this paper, the characteristics of the targeted problem are summarized; the gap between off-the-shelf methods and the practical need is then analyzed. A simple yet effective framework, called two-stage multi-fidelity surrogate model-assisted optimization (TMSO), is proposed to improve efficiency. TSMO is implemented by two state-of-the-art optimization algorithms and two real-world design cases demonstrate its effectiveness in practice. The research topics in multiobjective optimization and surrogate model-assisted optimization inspired by the TSMO framework is finally discussed.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Liu, Professor Bo
• Authors: Liu, B., Akinsolu, M. O., and Zhang, Q.
• College/School: College of Science and Engineering > School of Engineering > Systems Power and Energy
• ISBN: 9781728169293
• Copyright Holders: Copyright © 2020 IEEE
• First Published: First published in 2020 IEEE Congress on Evolutionary Computation (CEC)
• Publisher Policy: Reproduced in accordance with the publisher copyright policy

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