Constructing uniform design tables based on restart discrete dynamical evolutionary algorithm

IF 3.1 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Yuelin Zhao, Feng Wu, Yuxiang Yang, Xindi Wei, Zhaohui Hu, Jun Yan, Wanxie Zhong
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引用次数: 0

Abstract

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.

Abstract Image

基于重启离散动态进化算法构建统一设计表
生成统一设计表(UDT)是高效进行实验的第一步,也是最关键的步骤之一。因此,过去几十年来,统一设计表的构建受到了广泛关注。本文提出了一种构建统一设计表的新算法:重启离散动态进化算法(RDDE)。该算法基于精心设计的动态进化算法,利用离散舍入技术将连续变量转换为离散变量。考虑到 UDT 的优化是一个多目标优化问题,RDDE 采用弗里德曼排序法来选择综合比较排序较好的最优解。RDDE 还利用基于模拟退火的重启技术来选择控制参数,从而提高了算法跳出局部最优的能力。通过与最先进的 UDT 和两个实际工程实例的比较,验证了 RDDE 所构建的设计表的统一性。数值结果表明,RDDE 确实能在不同层次、不同系数和不同运行情况下构建出均匀性极佳的 UDT。特别是,RDDE 可以灵活地构建因子间隔不相等的 UDT,这是其他实验设计无法直接处理的。
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来源期刊
Soft Computing
Soft Computing 工程技术-计算机:跨学科应用
CiteScore
8.10
自引率
9.80%
发文量
927
审稿时长
7.3 months
期刊介绍: Soft Computing is dedicated to system solutions based on soft computing techniques. It provides rapid dissemination of important results in soft computing technologies, a fusion of research in evolutionary algorithms and genetic programming, neural science and neural net systems, fuzzy set theory and fuzzy systems, and chaos theory and chaotic systems. Soft Computing encourages the integration of soft computing techniques and tools into both everyday and advanced applications. By linking the ideas and techniques of soft computing with other disciplines, the journal serves as a unifying platform that fosters comparisons, extensions, and new applications. As a result, the journal is an international forum for all scientists and engineers engaged in research and development in this fast growing field.
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