A decomposition-based multi-objective particle swarm optimization algorithm with a local search strategy for key quality characteristic identification in production processes. (October 2022)

• Record Type: Journal Article
• Authors: He, Zhen; Hu, Hao; Zhang, Min; Zhang, Yang; Li, An-Da
• Is Part Of: Computers & industrial engineering. Volume 172:Part A(2022)
• Publication Date: 2022-10
• Subjects: Quality control -- Feature selection -- Particle swarm optimization -- Decomposition approaches -- Local search -- Multi-objective optimization; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2022.108617
• Languages: English
• ISSNs: 0360-8352

Abstract

Abstract: Identifying the key quality characteristics (KQCs) (including part and process parameters) in production processes is essential for quality control. In this paper, we propose a data-driven KQC identification method based on production process data. We model KQC identification as a multi-objective feature selection problem of maximizing the geometric mean (GM) and minimizing the number of selected QCs (features). GM can evaluate the importance of a QC subset by measuring its predictive ability for product quality. To solve this optimization model, we propose a multi-objective optimization algorithm called MOPSO-LS that combines particle swarm optimization (PSO) with a local search strategy. MOPSO-LS adopts a decomposition approach, i.e., Tchebycheff approach (TA), to update personal best positions ( p b e s t s) during the iterations. Thus, diversified and high quality solutions can be maintained by the p b e s t s of particles. Moreover, the local search strategy aims to update the non-dominated set found by MOPSO-LS during the iterations with two basic local search steps, i.e., a) adding and b) removing a feature, which can improve the convergence performance of MOPSO-LS. We have verified the proposed method on four production datasets. The experimental results indicate that MOPSO-LS can select a few KQCs with a good capability for predicting product quality, which shows the effectiveness of MOPSO-LS for KQC identification. Further comparisons show that MOPSO-LS obtains better search performance than four typical multi-objective optimization algorithms. Highlights: A multi-objective key quality characteristic (KQC) identification model is used. A multi-objective PSO algorithm based on decomposition is proposed for optimization. Decomposition of the optimization is achieved by a modified Tchebycheff approach. Local search is utilized to improve the search performance of the PSO algorithm. A few KQCs with good predictive performance for product quality can be identified.