A proximity-based surrogate-assisted method for simulation-based design optimization of a cylinder head water jacket
Ali Ahrari, Julian Blank, Kalyanmoy Deb, Xianren Li · Engineering Optimization · 2020
Ali Ahrariab , Julian Blankb* , Kalyanmoy Debb & Xianren Lica School of Engineering and Information Technology, University of New South Wales, Canberra, Australiab Department of Computer Science and Engineering, Michigan State University, East Lansing, USAc Research and Advanced Engineering, Ford Motor Company, Dearborn, USACONTACT Julian Blank [email protected] .ABSTRACTMany engineering design problems are associated with computationally expensive and time-consuming simulations for design evaluation. In such problems, each candidate design should be selected carefully, even though it means extra algorithmic complexity. This study develops the Proximity-based Surrogate-Assisted Evolutionary Algorithm (PSA-EA) that aims at handling both single-objective and multi-objective computationally expensive problems. It controls the trade-off between exploration and exploitation by defining proximity and trust regions around high-fidelity solutions. The proximity measure aims to maximize the diversity of information about specific regions of the search space and to improve the goodness of the surrogate for future cycles simultaneously. The method employs an ensemble of metamodels and a parallel infill criterion. PSA-EA is evaluated and compared to a recently developed surrogate-assisted evolutionary algorithm on ten test problems. Thereafter, a case study involving a multi-objective design optimization of the cylinder head water jacket of a vehicle engine is presented and discussed. Online supplemental data for this article can be accessed at https://doi.org/10.1080/0305215X.2020.1808972.