SADEA-II: A generalized method for efficient global optimization of antenna design. Issue 2 (20th November 2016)

• Record Type: Journal Article
• Authors: Liu, Bo; Koziel, Slawomir; Ali, Nazar
• Is Part Of: Journal of computational design and engineering. Volume 4:Issue 2(2017)
• Page Start: 86
• Page End: 97
• Publication Date: 2016-11-20
• Subjects: Antenna design optimization -- Antenna design automation -- Surrogate-model-assisted evolutionary algorithm -- Expensive optimization -- Multi-fidelity -- Variable fidelity -- Gaussian process; Engineering -- Data processing -- Periodicals; Computer-aided design -- Periodicals; Computer-aided design; Engineering -- Data processing; Electronic journals; Electronic journals; Periodicals; 620.0042
• DOI: 10.1016/j.jcde.2016.11.002
• Languages: English
• ISSNs: 2288-4300

Abstract

Abstract: Efficiency improvement is of great significance for simulation-driven antenna design optimization methods based on evolutionary algorithms (EAs). The two main efficiency enhancement methods exploit data-driven surrogate models and/or multi-fidelity simulation models to assist EAs. However, optimization methods based on the latter either need ad hoc low-fidelity model setup or have difficulties in handling problems with more than a few design variables, which is a main barrier for industrial applications. To address this issue, a generalized three stage multi-fidelity-simulation-model assisted antenna design optimization framework is proposed in this paper. The main ideas include introduction of a novel data mining stage handling the discrepancy between simulation models of different fidelities, and a surrogate-model-assisted combined global and local search stage for efficient high-fidelity simulation model-based optimization. This framework is then applied to SADEA, which is a state-of-the-art surrogate-model-assisted antenna design optimization method, constructing SADEA-II. Experimental results indicate that SADEA-II successfully handles various discrepancy between simulation models and considerably outperforms SADEA in terms of computational efficiency while ensuring improved design quality. Graphical abstract: