Deep belief network (DBN) is one of the most representative deep learning model. However, it has a disadvantage that the network structure and parameters are basically determined by experiences. In this paper, an improved quantum-inspired differential evolution, namely MSIQDE algorithm based on making use of merits of Mexh wavelet function, standard normal distribution, adaptive quantum state update and quantum non-gate mutation is proposed to avoid premature convergence and improve global search ability. Then the MSIQDE with global optimization ability is used to optimize the parameters of the DBN to construct an optimal DBN model, which is further applied to propose a new fault classification, namely MSIQDEDBN method. Finally, the vibration data of rolling bearings from the Case Western Reserve University and a real-world engineering application are carried out to verify the performance of the MSIQDE-DBN method. The experimental results show that the MSIQDE takes on better optimization performance and the MSIQDE-DBN can obtain higher classification accuracy than the other comparison methods.

With the continuous and rapid growth of air traffic demand, gate resource becomes a major bottleneck restricting airport development. Rational gate allocation is regarded as one of the most important means to solve this bottleneck. In this paper, in order to comprehensively considere different stakeholders, a three-objective gate allocation model is to consider a wider scope, in which the minimizing passenger walking distances, the most balanced idle time of each gate and the best full use of large gate are optimized simultaneously to improve the practical efficiency. To efficiently solve this model, an improved quantum evolutionary algorithm (QEA) based on the niche co-evolution strategy and enhanced particle swarm optimization (PSO), namely IPOQEA is designed. An IPOQEA-based gate allocation method is proposed to allocate the flights to suitable gates within different periods. Finally, the actual operation data of Baiyun Airport is used to validate the effectiveness of the proposed method. Comparison results show that the constructed model can address the passenger walking distances, robustness and costs in airport management. Moreover, the IPOQEA has better optimization ability in solving gate allocation problem. Therefore, the proposed gate allocation method has great potential for practical engineering since it can easily make decisions for airport managers.

In this paper, a large-scale multi-objective gate assignment model is constructed by considering the flight international and domestic attributes, task type, airline affiliation, and aircraft type. Then a multi-objective quantum-inspired evolutionary algorithm based on decomposition mechanism, namely MOQEA/D is developed to solve the constructed model effectively. Specifically, a new decomposition mechanism is designed to decompose the multi-objective GAP into several single-objective sub-GAPs. Each quantum bit string solves a single-objective sub-GAP independently. And a new optimal crossover strategy is proposed to limit the randomness of observation operations and maximize the preservation of excellent genes to further improve the optimization performance. Finally, the multi-objective knapsack problem and the multi-objective GAP are selected to verify the effectiveness of the MOQEA/D. The experiment results demonstrate that the MOQEA/D can effectively solve large-scale multi-objective knapsack problem and obtain ideal gate assignment results. It takes on very significance and application value in solving complex optimization problems.

In solving the portfolio optimization problem, the mean-semivariance (MSV) model is more complicated and time-consuming, and their relations are unbalanced because they conflict with each other due to return and risk. Therefore, in order to solve these existing problems, multi-strategy adaptive particle swarm optimization, namely APSO/DU, has been developed to solve the portfolio optimization problem. In the present study, a constraint factor is introduced to control velocity weight to reduce blindness in the search process. A dual-update (DU) strategy is based on new speed, and position update strategies are designed. In order to test and prove the effectiveness of the APSO/DU algorithm, test functions and a realistic MSV portfolio optimization problem are selected here. The results demonstrate that the APSO/DU algorithm has better convergence accuracy and speed and finds the least risky stock portfolio for the same level of return. Additionally, the results are closer to the global Pareto front (PF). The algorithm can provide valuable advice to investors and has good practical applications.

Hyperspectral image (HSI) spectral-spatial joint feature extraction methods generally suffer from low feature retention and weak spatial-spectral dependence, which will lead to single class feature confrontation (SCFC). To solve this problem, an unsupervised multivariate feature fusion network (M ³ FuNet) is developed in this paper. In M ³ FuNet, multiscale supervector matrix correction (MSMC) and multiscale random convolution dispersion (MRCD) is used as the spectral and spatial feature extraction method, and the feature retention of spectral and spatial features is improved to achieve feature calibration by feature fusion and decision fusion, called multivariate feature fusion. The MSMC is employed to correct supervector matrix to reduce the intraclass variance in superpixel homogeneous regions, overcome the phenomenon of supervector block drift (SvBD). The MRCD uses random convolution and gaussian smoothing to extract deep spatial features. Due to the similar feature representation ability of the MSMC and MRCD, the obtained spectral-spatial joint features have high feature retention and strong spectral-spatial dependence. Finally, this multivariate feature fusion network is used for realizing classification of HSI. Three commonly HSI datasets are used to validate the effectiveness of the M ³ FuNet. The experiment results show that the M ³ FuNet has more superior performance by comparing with several state-of-the-art HSI classification methods. The code of the proposed M ³ FuNet is available at http://github.com/aichou233/M3FuNet.