Dual-mode nested search method for categorical uncertain multi-objective optimization. Issue 10 (2nd October 2016)

• Record Type: Journal Article
• Authors: Tang, Long; Wang, Hu
• Is Part Of: Engineering optimization. Volume 48:Issue 10(2016)
• Page Start: 1759
• Page End: 1777
• Publication Date: 2016-10-02
• Subjects: categorical optimization -- uncertain optimization -- multi-objective optimization; Engineering design -- Periodicals; Mathematical optimization -- Periodicals; 620.0042
• DOI: 10.1080/0305215X.2015.1137564
• Languages: English
• ISSNs: 0305-215X

Abstract

Abstract : Categorical multi-objective optimization is an important issue involved in many matching design problems. Non-numerical variables and their uncertainty are the major challenges of such optimizations. Therefore, this article proposes a dual-mode nested search (DMNS) method. In the outer layer, kriging metamodels are established using standard regular simplex mapping (SRSM) from categorical candidates to numerical values. Assisted by the metamodels, a k -cluster-based intelligent sampling strategy is developed to search Pareto frontier points. The inner layer uses an interval number method to model the uncertainty of categorical candidates. To improve the efficiency, a multi-feature convergent optimization via most-promising-area stochastic search (MFCOMPASS) is proposed to determine the bounds of objectives. Finally, typical numerical examples are employed to demonstrate the effectiveness of the proposed DMNS method.