An approximate optimization strategy using refined hybrid metamodel. (4th September 2019)

• Record Type: Journal Article
• Authors: Lee, Kwon‐Hee; Jeong, Gyung‐Il; Lee, Seong‐Hyeong
• Is Part Of: Computational intelligence. Volume 36:Number 1(2020)
• Page Start: 35
• Page End: 54
• Publication Date: 2019-09-04
• Subjects: Hessian -- hybrid model -- kriging -- optimization -- RHMM (refined hybrid metamodel) -- RSM (response surface method); Artificial intelligence -- Periodicals; Computational linguistics -- Periodicals; 006.3
• DOI: 10.1111/coin.12237
• Languages: English
• ISSNs: 0824-7935

Abstract

Abstract: An approximate model called metamodel or surrogate model is a mathematical model that numerically approximates response of a system during an engineering simulation process or test. The introduction of a metamodel makes it possible to express response defined in the design problem as a simple mathematical function of design variables. A metamodel can be built with response surface method (RSM), kriging, neural network, radial basis function, and so on. Each method has its advantages and disadvantages. A combined metamodel called hybrid model, ensemble model, or multiple surrogates has been developed to maximize each metamodel's strength. The hybrid model of this research includes RSM and kriging. Besides, a strategy to refine the hybrid metamodel is implemented by reducing design space. In this process, information related to Hessian is utilized for an unconstrained optimization problem, on the contrary feasibility for a constrained optimization problem. This research presents a new hybrid metamodel‐based optimization strategy called refined hybrid metamodel. Five mathematical test problems, two‐bar design, spring design, and propeller shaft design problems are solved with the suggested method, verifying its usefulness. Most of the optimal results with the proposed method are closer to exact solutions with smaller function evaluations than existing methods.