An integrated model for maintenance policies and production scheduling based on immune–culture algorithm

The development of integrated modelling for maintenance policies of multi-component repairable system and production scheduling is challenging for two reasons. First, capturing dependency of this multi-component repairable system is difficult because different failure types associated with different components are under competing risks and their complicated relationships may lead to overall system dependency. Second, the integrated model is difficult to optimize because it is an NP-hard problem that exact optimization methods are intractable. For coping with these two difficulties, we propose a parametric statistical model using copula function to capture the overall system dependency. Under partially perfect maintenance policy at component-level, the likelihood functions for observed failures are derived and maximum likelihood method is used to estimate unknown parameters. Then relying on this parametric statistical model, the system hazard function is derived to depict the reliability-based imperfect preventive maintenance policy at system-level. Finally, to obtain the optimal solution(s) of the integrated model, we design an adaptive immune clone selection–culture algorithm, which is inspired from immune clone selection algorithm and culture algorithm. Results of the case study validate that our proposed maintenance policies and methodology have great advantages over the component-level or system-level maintenance policy and immune clone selection algorithm.

Discovering the Representative Subset with Low Redundancy for Hyperspectral Feature Selection

In this paper, a novel unsupervised band selection (BS) criterion based on maximizing representativeness and minimizing redundancy (MRMR) is proposed for selecting a set of informative bands to represent the whole hyperspectral image cube. The new selection criterion is denoted as the MRMR selection criterion and the associated BS method is denoted as the MRMR method. The MRMR selection criterion can evaluate the band subset’s representativeness and redundancy simultaneously. For one band subset, its representativeness is estimated by using orthogonal projection (OP) and its redundancy is measured by the average of the Pearson correlation coefficients among the bands in this subset. To find the satisfactory subset, an effective evolutionary algorithm, i.e., the immune clone selection (ICS) algorithm, is applied as the subset searching strategy. Moreover, we further introduce two effective tricks to simplify the computation of the representativeness metric, thus the computational complexity of the proposed method is reduced significantly. Experimental results on different real-world datasets demonstrate that the proposed method is very effective and its selected bands can obtain good classification performances in practice.

An Improved Immune Clone Selection Algorithm for Palm Bio-impedance Spectroscopy

A kind of effective feature model of palm BIS data is presented according to the features of Palm bio-impedance spectroscopy (BIS) data. Based on immune clone algorithm and least squares method, an improved palm BIS feature selection algorithm is established,which can be applied to obtain the optimal feature subset that can be completely represented the palm BIS data, Finally, the algorithm is compared with other algorithms. The experimental results show that the accuracy of the feature subset obtained by the algorithm has reached 93.2 in SVM classification algorithm test.Therefore, the algorithm in this article is valid and reliable , which is of high theoretical and practical value.