Constructing uniform design tables based on restart discrete dynamical evolutionary algorithm

被引：0

作者：

Zhao, Yuelin ^{[1
]}

Wu, Feng ^{[1
]}

Yang, Yuxiang ^{[1
]}

Wei, Xindi ^{[1
]}

Hu, Zhaohui ^{[1
]}

Yan, Jun ^{[1
]}

Zhong, Wanxie ^{[1
]}

机构：

[1] State Key Laboratory of Structural Analysis, Optimization and CAE Software for Industrial Equipment, Department of Engineering Mechanics, Faculty of Vehicle Engineering and Mechanics, Dalian University of Technology, Dalian

来源：

Soft Computing | 2024年 / 28卷 / 19期

基金：

中国国家自然科学基金;

关键词：

Discrete rounding; Dynamical optimization algorithm; Multi-objective optimization; Simulated annealing; Uniform design table;

D O I：

10.1007/s00500-024-09890-x

中图分类号：

学科分类号：

摘要：

Generating uniform design tables (UDTs) is the first step to experimenting efficiently and effectively, and is also one of the most critical steps. Thus, the construction of uniform design tables has received much attention over the past decades. This paper presents a new algorithm for constructing uniform design tables: restart discrete dynamical evolutionary algorithm (RDDE). This algorithm is based on a well-designed dynamical evolutionary algorithm and utilizes discrete rounding technology to convert continuous variables into discrete variables. Considering the optimization of UDT is a multi-objective optimization problem, RDDE uses Friedman rank to select the optimal solution with better comprehensive comparison ranking. RDDE also utilizes a simulated annealing-based restart technology to select control parameters, thereby increasing the algorithm's ability to jump out of local optima. Comparisons with state-of-the-art UDTs and two practical engineering examples are presented to verify the uniformity of the design table constructed by RDDE. Numerical results indicate that RDDE can indeed construct UDTs with excellent uniformity at different levels, factors, and runs. Especially, RDDE can flexibly construct UDTs with unequal intervals of factors that cannot be directly processed by other designs of experiment. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

引用

页码：11515 / 11534

页数：19

共 68 条

[1] Aarts E.H.L., van Laarhoven P.J.M., Simulated annealing: an introduction, Blackwell Publishing Ltd, 1, 43, pp. 31-52, (1989)
[2] Abadir K.M., Distaso W., Testing joint hypotheses when one of the alternatives is one-sided, J Econom, 140, 2, pp. 695-718, (2007)
[3] Abualigah L., Ekinci S., Izci D., Zitar R.A., Modified elite opposition-based artificial hummingbird algorithm for designing FOPID controlled cruise control system, Intel Autom Soft Comput, 38, 2, pp. 169-183, (2023)
[4] Agushaka J.O., Ezugwu A.E., Abualigah L., Dwarf mongoose optimization algorithm, Comput Methods Appl Mech Eng, 391, (2022)
[5] Agushaka J.O., Ezugwu A.E., Abualigah L., Gazelle optimization algorithm: a novel nature-inspired metaheuristic optimizer, Neural Comput Appl, 35, 5, pp. 4099-4131, (2023)
[6] Alam E., Feng Q.Y., Yang H., Fan J.X., Mumtaz S., Begum F., Synthesis of Fe3O4@mZrO2-Re (Re = Y/La/Ce) by using uniform design, surface response methodology, and orthogonal design & its application for As3+ and As5+ removal, Nanomaterials (basel, Switzerland), 11, 9, (2021)
[7] Cao H.R., Li L., MATLAB implementation of uniform design table, Stat Decis Making, 6, pp. 144-146, (2008)
[8] Chang Y., Jiao J.P., Liu X.C., Li G.H., He C.F., Wu B., Application of uniform experimental design in optimizing excitation parameters for magnetic frequency mixing measurements, Chin J Mech Eng, 33, 1, (2020)
[9] Che H.J., Wang J., A collaborative neurodynamic approach to global and combinatorial optimization, Neural Netw, 114, pp. 15-27, (2019)
[10] Chen W., Qi Z.F., Zhou Y.D., Constructing uniform designs under mixture discrepancy, Stat Probab Lett, 97, pp. 76-82, (2015)

← 1 2 3 4 5 6 7 →