The flexible job shop scheduling problem (FJSP), which is NP-hard, widely exists in many manufacturing industries. It is very hard to be solved. A multi-swarm collaborative genetic algorithm (MSCGA) based on the collaborative optimization algorithm is proposed for the FJSP. Multi-population structure is used to independently evolve two sub-problems of the FJSP in the MSCGA. Good operators are adopted and designed to ensure this algorithm to achieve a good performance. Some famous FJSP benchmarks are chosen to evaluate the effectiveness of the MSCGA. The adaptability and superiority of the proposed method are demonstrated by comparing with other reported algorithms.

In order to solve the flexible job shop scheduling problem with variable batches, we propose an improved multi-objective optimization algorithm, which combines the idea of inverse scheduling. First, a flexible job shop problem with the variable batches scheduling model is formulated. Second, we propose a batch optimization algorithm with inverse scheduling in which the batch size is adjusted by the dynamic feedback batch adjusting method. Moreover, in order to increase the diversity of the population, two methods are developed. One is the threshold to control the neighborhood updating, and the other is the dynamic clustering algorithm to update the population. Finally, a group of experiments are carried out. The results show that the improved multi-objective optimization algorithm can ensure the diversity of Pareto solutions effectively, and has effective performance in solving the flexible job shop scheduling problem with variable batches.

Multi-manned assembly line, which is broadly utilized to assemble high volume products such as automobiles and trucks, allows a group of workers to assemble different tasks simultaneously in a multi-manned workstation. This additional characteristic of parallel operators increases the complexity of the traditional NP-hard assembly line balancing problem. Hence, this paper formulates the Type-I multi-manned assembly line balancing problem to minimize the total number of workstations and operators, and develops an efficient migrating birds optimization algorithm embedded into an idle time reduction method. In this algorithm, a new decoding mechanism is proposed which reduces the sequence-dependent idle time by some task assignment rules; three effective neighborhoods are developed to make refinement of existing solutions in the bird improvement phases; and temperature acceptance and competitive mechanism are employed to avoid being trapped in the local optimum. Comparison experiments suggest that the new decoding and improvements are effective and the proposed algorithm outperforms the compared algorithms.

In a typical discrete manufacturing process, a new type of reconfigurable production line is introduced, which aims to help small- and mid-size enterprises to improve machine utilization and reduce production cost. In order to effectively handle the production scheduling problem for the manufacturing system, an improved multi-objective particle swarm optimization algorithm based on Brownian motion (MOPSO-BM) is proposed. Since the existing MOPSO algorithms are easily stuck in the lo-cal optimum, the global search ability of the proposed method is enhanced based on the random motion mechanism of the BM. To further strengthen the global search capacity, a strategy of fitting the inertia weight with the piecewise Gaussian cumulative distribution function (GCDF) is included, which helps to maintain an excellent convergence rate of the algorithm. Based on the commonly used indicators generational distance (GD) and hypervolume (HV), we compare the MOPSO-BM with several other latest algorithms on the benchmark functions, and it shows a better overall performance. Furthermore, for a real reconfigurable production line of smart home appliances, three algorithms, namely non-dominated sorting genetic algorithm-II (NSGA-II), decomposition-based MOPSO (dMOPSO) and MOPSO-BM, are applied to tackle the scheduling problem. It is demonstrated that MOPSO-BM outperforms the others in terms of convergence rate and quality of solutions.

Surrogate models have shown to be effective in assisting evolutionary algorithms (EAs) for solving computationally expensive complex optimization problems. However, the effectiveness of the existing surrogate-assisted evolutionary algorithms still needs to be improved. A data-driven evolutionary sampling optimization (DESO) framework is proposed, where at each generation it randomly employs one of two evolutionary sampling strategies, surrogate screening and surrogate local search based on historical data, to effectively balance global and local search. In DESO, the radial basis function (RBF) is used as the surrogate model in the sampling strategy, and different degrees of the evolutionary process are used to sample candidate points. The sampled points by sampling strategies are evaluated, and then added into the database for the updating surrogate model and population in the next sampling. To get the insight of DESO, extensive experiments and analysis of DESO have been performed. The proposed algorithm presents superior computational efficiency and robustness compared with five state-of-the-art algorithms on benchmark problems from 20 to 200 dimensions. Besides, DESO is applied to an airfoil design problem to show its effectiveness.

It is of great significance to carry out effective scheduling for the carrier-based aircraft flight deck operations. In this paper, the precedence constraints and resource constraints in flight deck operations are analyzed, then the model of the multi-aircraft integrated scheduling problem with transfer times (MAISPTT) is established. A dual population multi-operator genetic algorithm (DPMOGA) is proposed for solving the problem. In the algorithm, the dual population structure and random-key encoding modified by starting/ending time of operations are adopted, and multiple genetic operators are self-adaptively used to obtain better encodings. In order to conduct the mapping from encodings to feasible schedules, serial and parallel scheduling generation scheme-based decoding operators, each of which adopts different justified mechanisms in two separated populations, are introduced. The superiority of the DPMOGA is verified by simulation experiments.

Considering the flexible attitude maneuver and the narrow field of view of agile Earth observation satellite (AEOS) together, a comprehensive task clustering (CTC) is proposed to improve the observation scheduling problem for AEOS (OSPFAS). Since the observation scheduling problem for AEOS with comprehensive task clustering (OSWCTC) is a dynamic combination optimization problem, two optimization objectives, the loss rate (LR) of the image quality and the energy consumption (EC), are proposed to format OSWCTC as a bi-objective optimization model. Harnessing the power of an adaptive large neighborhood search (ALNS) algorithm with a nondominated sorting genetic algorithm II (NSGA-II), a bi-objective optimization algorithm, ALNS+NSGA-II, is developed to solve OSWCTC. Based on the existing instances, the efficiency of ALNS+NSGA-II is analyzed from several aspects, meanwhile, results of extensive computational experiments are presented which disclose that OSPFAS considering CTC produces superior outcomes.

The multi-compartment electric vehicle routing problem (EVRP) with soft time window and multiple charging types (MCEVRP-STW&MCT) is studied, in which electric multi-compartment vehicles that are environmentally friendly but need to be recharged in course of transport process, are employed. A mathematical model for this optimization problem is established with the objective of minimizing the function composed of vehicle cost, distribution cost, time window penalty cost and charging service cost. To solve the problem, an estimation of the distribution algorithm based on Lévy flight (EDA-LF) is proposed to perform a local search at each iteration to prevent the algorithm from falling into local optimum. Experimental results demonstrate that the EDA-LF algorithm can find better solutions and has stronger robustness than the basic EDA algorithm. In addition, when comparing with existing algorithms, the result shows that the EDA-LF can often get better solutions in a relatively short time when solving medium and large-scale instances. Further experiments show that using electric multi-compartment vehicles to deliver incompatible products can produce better results than using traditional fuel vehicles.

The hybrid waveform of linear frequency modulation and binary phase shift keying (LFM-BPSK) can take advantages of the LFM and BPSK signals, and reduce the defects of them. However, with the development of interception technology for the LFM-BPSK signal, the application of the signal is limited. In this paper, to improve the anti-interception performance of the hybrid waveform, a new waveform of LFM-BPSK with the varying chirp rate (denoted as VCR-LFM-BPSK) is designed. In this design, based on the working principle of the interception frame for the LFM-BPSK signal, different chirp rates are introduced in different sub-pulses to prevent the signal from being intercepted by the frame. Then, to further improve the anti-interception performance of the VCR-LFM-BPSK signal, the chirp rates are optimized by minimizing the interception capability of the interceptor. Moreover, based on the VCR-LFM-BPSK signal with the optimized chirp rates, the binary phases are designed via a multi-objective Pareto optimization to improve the capabilities of autocorrelation and spectrum. Simulation results demonstrate that the designed VCR-LFM-BPSK signal outperforms the traditional LFM-BPSK signal in countering the advanced interception technologies.

Software defect prediction (SDP) is used to perform the statistical analysis of historical defect data to find out the distribution rule of historical defects, so as to effectively predict defects in the new software. However, there are redundant and irrelevant features in the software defect datasets affecting the performance of defect predictors. In order to identify and remove the redundant and irrelevant features in software defect datasets, we propose ReliefF-based clustering (RFC), a cluster-based feature selection algorithm. Then, the correlation between features is calculated based on the symmetric uncertainty. According to the correlation degree, RFC partitions features into k clusters based on the k-medoids algorithm, and finally selects the representative features from each cluster to form the final feature subset. In the experiments, we compare the proposed RFC with classical feature selection algorithms on nine National Aeronautics and Space Administration (NASA) software defect prediction datasets in terms of area under curve (AUC) and F-value. The experimental results show that RFC can effectively improve the performance of SDP.

Pilot contamination can spoil the accuracy of channel estimation and then has become one of the key problems influencing the performance of massive multiple input multiple output (MIMO) systems. This paper proposes a method based on cell classification and users grouping to mitigate the pilot contamination in multi-cell massive MIMO systems and improve the spectral efficiency. The pilots of the terminals are allocated one-bit orthogonal identifier to diminish the cell categories by the operation of exclusive OR (XOR). At the same time, the users are divided into edge user groups and central user groups according to the large-scale fading coefficients by the clustering algorithm, and different pilot sequences are assigned to different groups. The simulation results show that the proposed method can effectively improve the spectral efficiency of multi-cell massive MIMO systems.

Multi-access interference (MAI) is the main source limiting the capacity and quality of the multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) system which fulfills the demand of high-speed transmission rate and high quality of service for future underwater acoustic (UWA) communication. Therefore, multi-user detection (MUD) is needed at the receiver of the MIMO-OFDM system to suppress the effect of MAI. In this research, MUD is achieved by using a criterion based adaptive recursive successive interference cancellation (RSIC) scheme at the receiver of a MIMO-OFDM system whose transceiver model in underwater communication is implemented by using the Bellhop simulation system. The proposed scheme estimates and eliminates the MAI through user signal detection and subtraction from received signals at the receiver of the MIMO-OFDM system in underwater environment. The bit error rate (BER) performance of the proposed scheme is analyzed by using weight filtering and weight selection criteria. By Matlab simulation, it is shown that the BER performance of the proposed scheme outperforms the conventional matched filter (MF) detector, the adaptive successive interference cancellation (SIC) scheme, and the adaptive RSIC scheme in the UWA network.

For forward-looking array synthetic aperture radar (FASAR), the scattering intensity of ground scatterers fluctuates greatly since there are kinds of vegetations and topography on the surface of the ground, and thus the signal-to-noise ratio (SNR) of its echo signals corresponding to different vegetations and topography also varies obviously. Owing to the reason known to all, the performance of the sparse reconstruction of compressed sensing (CS) becomes worse in the case of lower SNR, and the quality of the sparse three-dimensional imaging for FASAR would be affected significantly in the practical application. In this paper, the spatial continuity of the ground scatterers is introduced to the sparse recovery algorithm of CS in the three-dimensional imaging for FASAR, in which the weighted least square method of the cubic interpolation is used to filter out the bad and isolated scatterer. The simulation results show that the proposed method can realize the sparse three-dimensional imaging of FASAR more effectively in the case of low SNR.

Inverse synthetic aperture radar (ISAR) imaging of near-field targets is potentially useful in some specific applications, which makes it very important to efficiently produce high-quality image of the near-field target. In this paper, the simplified target model with uniform linear motion is applied to the near-field target imaging, which overcomes the complexity of the traditional near-field imaging algorithm. According to this signal model, the method based on coordinate conversion and image interpolation combined with the range-Doppler (R-D) algorithm is proposed to correct the near-field distortion problem. Compared with the back-projection (BP) algorithm, the proposed method produces better focused ISAR images of the near-field target, and decreases the computation complexity significantly. Experimental results of the simulated data have demonstrated the effectiveness and robustness of the proposed method.

To address the problem of the weak anti-noise and macro-trend extraction abilities of the current methods for identifying radar antenna scan type, a recognition method for radar antenna scan types based on limited penetrable visibility graph (LPVG) is proposed. Firstly, seven types of radar antenna scans are analyzed, which include the circular scan, sector scan, helical scan, raster scan, conical scan, electromechanical hybrid scan and two-dimensional electronic scan. Then, the time series of the pulse amplitude in the radar reconnaissance receiver is converted into an LPVG network, and the feature parameters are extracted. Finally, the recognition result is obtained by using a support vector machine (SVM) classifier. The experimental results show that the recognition accuracy and noise resistance of this new method are improved, where the average recognition accuracy for radar antenna type is at least 90% when the signal-to-noise ratio (SNR) is 5 dB and above.

This paper investigates the problem of distributed cooperative guidance law design for multiple anti-ship missiles in the three-dimensional (3-D) space hitting simultaneously the same target with considering the desired terminal impact angle constraint. To address this issue, the problem formulation including 3-D nonlinear mathematical model description, and communication topology are built firstly. Then the consensus variable is constructed using the available information and can reach consensus under the proposed acceleration command along the line-of-sight (LOS) which satisfies the impact time constraint. However, the normal accelerations are designed to guarantee the convergence of the LOS angular rate. Furthermore, consider the terminal impact angle constraints, a nonsingular terminal sliding mode (NTSM) control is introduced, and a finite time convergent control law of normal acceleration is proposed. The convergence of the proposed guidance law is proved by using the second Lyapunov stability method, and numerical simulations are also conducted to verify its effectiveness. The results indicate that the proposed cooperative guidance law can regulate the impact time error and impact angle error in finite time if the connecting time of the communication topology is longer than the required convergent time.

A novel non-contact spacecraft architecture with the extended stochastic state observer for disturbance rejection control of the gravity satellite is proposed. First, the precise linear driving non-contact voice-coil actuators are used to separate the whole spacecraft into the non-contact payload module and the service module, and to build an ideal loop with precise dynamics for disturbance rejection control of the payload module. Second, an extended stochastic state observer is enveloped to construct the overall nonlinear external terms and the internal coupled terms of the payload module, enabling the controller design of the payload module turned into the linear form with simple bandwidth-parameterization tuning in the frequency domain. As a result, the disturbance rejection control of the payload module can be explicitly achieved in a timely manner without complicated tuning in actual implementation. Finally, an extensive numerical simulation is conducted to validate the feasibility and effectiveness of the proposed approach.

Trajectory clustering can identify the flight patterns of the air traffic, which in turn contributes to the airspace planning, air traffic flow management, and flight time estimation. This paper presents a semantic-based trajectory clustering method for arrival aircraft via new proposed trajectory representation. The proposed method consists of four significant steps: representing the trajectories, grouping the trajectories based on the new representation, measuring the similarities between different trajectories through dynamic time warping (DTW) in each group, and clustering the trajectories based on k-means and density-based spatial clustering of applications with noise (DBSCAN). We take the inbound trajectories toward Shanghai Pudong International Airport (ZSPD) to carry out the case studies. The corresponding results indicate that the proposed method could not only distinguish the particular flight patterns, but also improve the performance of flight time estimation.

This paper presents a new subband adaptive filter (SAF) algorithm for system identification scenario under impulsive interference, named generalized continuous mixed p-norm SAF (GCMPN-SAF) algorithm. The proposed algorithm uses a GCMPN cost function to combat the impul-sive interference. To further accelerate the convergence rate in the sparse and the block-sparse system identification processes, the proportionate versions of the proposed algorithm, the L₀-norm GCMPN-SAF (L₀-GCMPN-SAF) and the block-sparse GCMPN-SAF (BS-GCMPN-SAF) algorithms are also developed. Moreover, the convergence analysis of the proposed algorithm is provided. Simulation results show that the proposed algorithms have a better performance than some other state-of-the-art algorithms in the literature with respect to the convergence rate and the tracking capability.

Target maneuver trajectory prediction plays an important role in air combat situation awareness and threat assessment. To solve the problem of low prediction accuracy of the traditional prediction method and model, a target maneuver trajectory prediction model based on phase space reconstruction-radial basis function (PSR-RBF) neural network is established by combining the characteristics of trajectory with time continuity. In order to further improve the prediction performance of the model, the rival penalized competitive learning (RPCL) algorithm is introduced to determine the structure of RBF, the Levenberg-Marquardt (LM) and the hybrid algorithm of the improved particle swarm optimization (IPSO) algorithm and the k-means are introduced to optimize the parameter of RBF, and a PSR-RBF neural network is constructed. An independent method of 3D coordinates of the target maneuver trajectory is proposed, and the target manuver trajectory sample data is constructed by using the training data selected in the air combat maneuver instrument (ACMI), and the maneuver trajectory prediction model based on the PSR-RBF neural network is established. In order to verify the precision and real-time performance of the trajectory prediction model, the simulation experiment of target maneuver trajectory is performed. The results show that the prediction performance of the independent method is better, and the accuracy of the PSR-RBF prediction model proposed is better. The prediction confirms the effectiveness and applicability of the proposed method and model.