Hybrid Single and Multiobjective optimization for Engineering Design without Exact Specifications
Liu, Bo, Akinsolu, Mobayode O. and Zhang, Qingfu (2020) Hybrid Single and Multiobjective optimization for Engineering Design without Exact Specifications. In: 2020 IEEE Congress on Evolutionary Computation (CEC), 19-24 July 2020, Glasgow, UK.
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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 or Workshop Item (Paper) |
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| Keywords: | Multi-fidelity optimization, engineering optimization, multiobjective, simulation-based optimization, surrogate model, MOEA/D, surrogate model-aware evolutionary search. |
| Divisions: | Applied Science, Computing and Engineering |
| Depositing User: | Hayley Dennis |
| Date Deposited: | 26 Aug 2021 11:40 |
| Last Modified: | 26 Aug 2021 11:40 |
| URI: | https://wrexham.repository.guildhe.ac.uk/id/eprint/17803 |
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