In this paper, we propose a method for classifying tea quality levels based on near-infrared spectroscopy. Firstly, the absorbance spectra of Huangshan Maofeng tea samples were obtained in a wavenumber range of 10,000~4000 cm⁻¹ using near-infrared spectroscopy. The spectral data were then converted to transmittance and smoothed using the Savitzky–Golay (SG) algorithm. The denoised transmittance spectra were dimensionally reduced using principal component analysis (PCA). The characteristic variables obtained using PCA were used as the input variables and the tea level was used as the output to establish a support vector machine (SVM) classification model. The penalty factor c and the kernel function parameter g in the SVM model were optimized using particle swarm optimization (PSO) and comprehensive-learning particle swarm optimization (CLPSO) algorithms. The final experimental results show that the CLPSO-SVM method had the best classification performance, and the classification accuracy reached 99.17%.

A colored traveling salesman problem (CTSP) is a generalization of the well-known multiple traveling salesman problem. In our prior CTSP, each salesman is allocated a particular color and each city, carrying 1, 2, or all salesmen's colors depending on the problem types, allows any salesmen with the same color to visit exactly once. This paper presents a more common CTSP, in which city colors are diverse, i.e., each city has one to all salesmen's colors while other elements of the problem keeps unchanged. It is a generalization of the existing CTSPs, i.e., the radial and serial ones, and can be used to model the scheduling problems with different accessibility of jobs toward executors. A city color matrix is introduced to describe the accessibility difference of cities to all salesmen. Since CTSP is NP-hard, this paper presents a variable neighborhood search (VNS) approach, instead of computationally intractable exact solutions. First, the repetitive solution space due to the dual-chromosome encoding for the prior genetic algorithms can be entirely avoided by using direct-route encoding. Then, a two-stage greedy initialization algorithm is utilized by VNS to generate the initial solution. A city removal mechanism and a reinsertion operation are introduced to change the neighborhood space of the current solution and 2-opt method is adopted for the local search. Extensive simulation is conducted and the results show that the proposed VNS is an efficient heuristics to solve CTSP.

Cluster analysis is a data mining technique that has been widely used to exploit useful information in a great amount of data. Because of their evaluation mechanism based on an intracluster distance (ICD) function, traditional single-objective clustering algorithms are not appropriate for not-well-separated data. Specifically, they may easily result in the drop of the optimal solution accuracy on their late stages of search when dealing with the latter. To overcome the problem, in this paper a novel index reflecting the similarity of data within a cluster is presented and called intracluster cohesion (ICC). However, if a multiobjective method is used to cluster with ICD and ICC as the specified objectives, its clustering accuracy may depend on one's experience. Motivated by these, we propose an accelerated two-stage particle swarm optimization (ATPSO) in which K-means is utilized to accelerate particles' convergence during the population initialization. Its clustering process consists of two stages. First, the main objective of minimizing ICD is to execute preliminary clustering; second, ICC is optimized to promote the clustering accuracy. Extensive experiments with the help of 17 open-source clustering sets in various geometric distributions are conducted. The results show that ATPSO outperforms PSO, K-means PSO (KPSO), chaotic PSO (CPSO), and accelerated CPSO in terms of accuracy, and its efficiency is approximate to that of KPSO. Its convergence trend indicates that the adoption of the proposed ICC contributes to the clustering accuracy. Remarkably, compared with the Pareto-based multiobjective PSO, ATPSO can detect clusters more accurately and quickly through the proposed two-stage search.

A colored traveling salesman problem (CTSP) is a generalization of the well-known multiple traveling salesman problem. It utilizes colors to differentiate the accessibility of its cities to its salesmen. In our prior work, CTSPs are formulated over graphs associated with a city-color matrix. This work redefines a general colored traveling salesman problem (GCTSP) in the framework of hypergraphs and reveals several important properties of GCTSP. In GCTSP, the setting of city colors is richer than that in CTSPs. As results, it can be used to model and address various complex scheduling problems. Then, a Delaunay-triangulation-based Variable Neighborhood Search (DVNS) algorithm is developed to solve large-scale GCTSPs. At the beginning stage of DVNS, a divide and conquer algorithm is exploited to prepare a Delaunay candidate set for lean insertion. Next, the incumbent solution is perturbed by utilizing greedy multi-insertion and exchange mutation to obtain a variety of neighborhoods. Subsequently, 2-opt and 3-opt are used for local search in turn. Extensive experiments are conducted for many large scale GCTSP cases among which two maximal ones are up to 33000+ cities for 4 salesmen and 240 salesmen given 11000+ cities, respectively. The results show that the proposed method outperforms the existing four genetic algorithms and two VNS methods in terms of search ability and convergence rate.

The advent of reconfigurable manufacturing systems (RMSs) has given rise to a challenging problem, i.e., how to reconfigure rapidly and validly a RMS supervisory controller in response to frequent changes in the manufacturing system configuration driven by fluctuating market. This paper presents an improved net rewriting system (INRS)-based method for automatic reconfiguration of Petri net (PN) supervisory controllers for RMS. We begin with presenting the INRS which overcomes the limitations of the net rewriting system and can dynamically change the structure of a PN without damaging its important behavioral properties. Based on INRS, a method for design reconfigurable PN controllers of RMS is introduced. Subsequently, we presented an INRS-based method for rapidly automatic reconfiguration of this class of PN controllers. In the reconfiguration method, changes in a RMS configuration can be formalized and act on an existing controller to make it reconfigure rapidly into a new one. Noticeably, no matter the design or reconfiguration, the expected behavioral properties of the resultant PN controllers are guaranteed. Thus, efforts for verification of the results can be avoided naturally. We also illustrate the reconfiguration of a PN controller for a reconfigurable manufacturing cell.