Multi-objective particle swarm optimization for key quality feature selection in complex manufacturing processes

In this paper, a feature selection (FS) method is proposed to identify key quality features (KQFs) in complex manufacturing processes. We propose a multi-objective binary particle swarm optimization algorithm, called MPBPSO, with three new components to optimize a bi-objective FS model of maximizing the geometric mean (GM) measure and minimizing the number of selected features. First, MPBPSO uses a modified probability-based solution update (PSU) mechanism which utilizes a flipping vector to update particles. A mutation operator with three basic operations, i.e., add, eliminate, and interchange, is also utilized in MPBPSO to improve the exploration performance. Second, a strategy combining the Pareto dominance concept with a distance measure is proposed for MPBPSO to update (phest)(personal best position). Finally, a selection strategy based on the roulette wheel selection is proposed to determine the (gbest) (global best position) from the non-dominated set during iterations. The experimental results on four datasets have shown that the proposed FS method can identify a small number of KQFs that have good predictive ability for product quality. Further analysis indicates that MPBPSO obtains better search performance than eight benchmark optimization algorithms and the new components in MPBPSO are effective for improving its search performance.

Automated summary

This paper proposes a feature selection method to identify key quality features in complex manufacturing processes. A multi-objective binary particle swarm optimization algorithm is proposed, which includes three new components to optimize a bi-objective feature selection model. Experimental results show that this method can identify a small number of key quality features with good predictive ability.