Development of a soft computing-based framework for engineering design optimisation with quantitative and qualitative search spaces |
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| Affiliation: | 1. Department of Enterprise Integration, School of Industrial and Manufacturing Science, Cranfield University, Building 53, Cranfield, Bedford MK43 0AL, UK;2. Corus R, D and T, Swinden Technology Center, Rotherham S60 3AR, UK;1. College of Information and Control Engineering, China University of Petroleum, Qingdao, PR China;2. Department of Computing, The Hong Kong Polytechnic University, Kowloon, Hong Kong;3. The Hong Kong Polytechnic University Shenzhen Research Institute, Shenzhen, PR China;1. Università degli Studi di Napoli Federico II, Dipartimento di Architettura, Via Monteoliveto 3, 80134 Napoli, Italy;2. Università degli Studi di Napoli Federico II, Centro Interdipartimentale di Ricerca per l’Analisi e la Progettazione Urbana Luigi Pisciotti, Via Toledo 402, 80134 Napoli, Italy;3. Università degli Studi di Salerno, Dipartimento di Informatica, Via Ponte don Melillo, 80084 Fisciano, Italy;1. School of Environmental Science and Engineering, Sun Yat-Sen University, Guangzhou, People''s Republic of China;2. Key Laboratory of Solid Waste Treatment and Resource Recycle, Ministry of Education, People''s Republic of China;3. School of Environmental Science and Engineering, Yangzhou University, 196 Huayang Road, Yangzhou 225000, People''s Republic of China;4. School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, People''s Republic of China;1. TEBE Research group, Department of Energetics, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy;2. ICIEE, Department of Civil Engineering, Technical University of Denmark, Nils Koppels Allé Building 402, 2800 Kgs. Lyngby, Denmark |
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| Abstract: | Most real world engineering design optimisation approaches reported in the literature aim to find the best set of solutions using computationally expensive quantitative (QT) models without considering the related qualitative (QL) effect of the design problem simultaneously. Although, the QT models provide various detailed information about the design problem, unfortunately, these approaches can result in unrealistic design solutions. This paper presents a soft computing-based integrated design optimisation framework of QT and QL search spaces using meta-models (design of experiment, DoE). The proposed approach is applied to multi-objective rod rolling problem with promising results. The paper concludes with a detailed discussion on the relevant issues of integrated QT and QL design strategy for design optimisation problems outlining its strengths and challenges. |
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