Air Target Threat Assessment Based on Improved Moth Flame Optimization‐Gray Neural Network Model

Purpose This paper aims to propose an improved Moth Flame Optimization (I-MFO) algorithm to optimize the cost-oriented two-sided assembly line balancing (2S-ALB). Prior to the decision to assemble a new product, the manufacturer will carefully study and optimize the related cost to set up and run the assembly line. For the first time in ALB, the power cost is modeled together with the equipment, set up and labor costs. Design/methodology/approach I-MFO was proposed by introducing a global reference flame mechanism to guide the global search direction. A set of benchmark problems was used to test the I-MFO performance. Apart from the benchmark problems, a case study from a body shop assembly was also presented. Findings The computational experiment indicated that the I-MFO obtained promising results compared to comparison algorithms, which included the particle swarm optimization, Cuckoo Search and ant colony optimization. Meanwhile, the results from the case study showed that the proposed cost-oriented 2S-ALB model was able to assist the manufacturer in making better decisions for different planning periods. Originality/value The main contribution of this work is the global reference flame mechanism for MFO algorithm. Furthermore, this research introduced a new cost-oriented model that considered power consumption in the assembly line design.

An improved moth-flame optimization (IMFO) algorithm is proposed to increase the location accuracy of a vision measurement system. This algorithm can optimize the initial pose parameters by improving a series of random solutions to the required precision. A measurement experiment system of space manipulator is designed to precision test. The IMFO algorithm is evaluated on 23 benchmark functions and measurement experiments for pose, and the results are verified by a comparative study with self-adaptive differential evolution (SaDE), moth-flame optimization (MFO), and proactive particle swarm optimization (PPSO). The statistical results of the benchmark functions show that the IMFO algorithm can provide very promising and competitive results. Additionally, the experimental results of pose measurement show that the accuracy of the IMFO algorithm is approximately twice higher than that of other three algorithms. All in all, the experiments indicate that the IMFO algorithm has a good optimization ability to complete the visual identification accurately.

The traditional threat assessment (TA) methods are confronted with the problems that most of them only focus on the static threat of a single target and the threshold of threat degree fusion is hard to set. For this reason, a novel DBN (dynamic Bayesian network) based TA algorithm is proposed. In the proposed algorithm, firstly, DBN is constructed with various factors, i.e., terrain, weather, time, relative strength, distance and velocity vector, for the TA of group targets. Then, the fast approximate inference is implemented according to Markov property. Finally, the probabilities of threat degrees are integrated into the continuous threat index and the discrete threat degree. Simulation results show that the proposed algorithm can be used to reliably and dynamically evaluate the threat of group targets in complex environment.

Space relays are affected by many nonlinear elements during storage, and the reason for predicting time series is to achieve nonlinear mapping. Combining artificial neural networks and grey system theory, we built a grey artificial neural network (GANN) model. The model effectively combined the characteristics of artificial-neural-network nonlinear adaptability and the characteristics of grey theory weakening data sequence volatility integration. We predicted the degradation value of the closing time of measured data in a relay accelerated storage test by using a variety of grey models and GANN models. By comparing several forecasting methods, the results showed the proposed grey neural network model has higher precision and is more accurate than a single grey model. The method also provides new ideas and methods for the life prediction of relay storage acceleration tests.

The Moth flame optimization algorithm (MFO) is a nature-inspired optimization algorithm which imitates moths darts into the fire. Similarly, to other meta-heuristic algorithm, MFO still has the disadvantage of getting into the local best and the convergence rate cannot be satisfying. In this paper a Multi-operator Moth Flame Optimization algorithm (MOMFO) is proposed to improve the MFO algorithm, which combines Elite Search strategy, Adaptive control strategy and Chaos search strategy to enhance MFO.A two-stage strategy is used to balance the global search and local search ability, which makes the algorithm have better search performance and accuracy. The proposed MOMFO is benchmarked on eight well-known test functions and real-world problem, and the results show that the algorithm is effective in solving complex nonlinearity problems.

In this article, the optimal allocation of compensation devices in power systems is determined using Improved Moth-flame Optimization (IMFO) algorithm. The main objective of this study is to maximize power system loadability, reduce total power system losses and enhance the voltage profile through finding the optimal locations and sizes of Static VAR Compensator (SVC) and Thyristor Controlled Compensator (TCSC). The methodology of this article is based on Continuation Power Flow (CPF) which is merged with IMFO in Optimum Power Flow (OPF) problem restricted by equality and inequality constraints. The proposed algorithm is implemented to regulate the required control variables. Both of single and multi-objective functions are applied on 57-IEEE bus test system to prove the performance of the proposed algorithm. The acquired simulation results by IMFO are compared with Moth Flame Optimization technique (MFO) and Multi Verse Optimizer (MVO) to emphasize the robustness of the proposed algorithm.

In this research we study the optimization problem of path planning in an oceanic ecosystem with a Static current in the presences of various obstacles with the autonomy of Autonomous underwater vehicles (AUV). We introduce an improved Moth flame optimization algorithm called refraction principle and opposite-based-learning Moth flame optimization (ROBL-MFO) in a Static current environment. This algorithm addresses the differences between the traditional Moth flame optimization (MFO) algorithm. First, it adjusts the moth position update formula we deal with the term of the historically optimal flame average, and improves the convergence velocity of the algorithm. Secondly, it uses a random inverse learning strategy to narrow down the algorithm search. Finally, the principle of refraction provides the algorithm with the ability to jump from the local optima and help the algorithm avoid premature convergence. The numerical results show that the ROBL-MFO improves the classical MFO, in terms of optimization criteria.KeywordsAutonomous underwater vehiclesPath planningStatic currentRefraction principle and opposite-based learning - Moth flame optimization

In recent years, with the transformation of our country’s economic structure, the consumption level of residents has gradually increased, and the volume of various consumer credit businesses of commercial banks has increased sharply, which has made the bank’s risk management and control work facing huge challenges. Traditional risk assessment methods mainly rely on the market experience of credit personnel. The results of risk assessment are greatly affected by personal subjective factors. In addition, in the face of increasing data volume and business volume, traditional assessment methods are inefficient, have long business cycles, and are accurate. It is difficult to guarantee. Therefore, this article aims to study the application of electricity data in personal credit risk assessment of commercial banks. On the basis of analyzing the causes of personal credit risk, the characteristics of personal credit risk and the application of electricity data in personal credit risk assessment, a personal credit assessment model is established through the data mining method of neural network, and the model is predicted. The prediction results show that the overall prediction accuracy rate of the model on the training data set is 86%, and the prediction accuracy rate of the test data set is 78%. The neural network model has high prediction accuracy, low data requirements and good results.

A threat assessment(TA) method based on intuitionistic fuzzy sets(IFS) is proposed in view of the threat assessment of air defense operations. Firstly, a novel nonlinear programming model based on intuitionistic fuzzy entropy(IFE) is constructed to obtain the objective weight of the target attributes, and the subjective correction is made by using the analytic hierarchy process(AHP). Secondly, a model based on technique for order preference by similarity to ideal solution (TOPSIS) and IFS is established to investigate the position relation of the sequence. And a grey correlation model based on IFS is proposed to investigate the curve shape of the sequence. On this basis, taking the preferences of the decision-makers(DMs) into consideration, the integration degree of two methods above can be adjusted flexibly by different preferences. we can obtain the ultimate ranking result by the normalized threat assessment values of the targets. Finally, the simulation shows the flexibility and reliability of the proposed method, which provides more reasonable auxiliary decision-making for air defense combat decision-makers.

Time series prediction of wind speed has been widely used in wind power generation. The volatility and instability of wind speed have a large negative impact on wind turbines and power systems, which can lead to grid collapse in severe cases. Therefore, accurate wind speed prediction is crucial for wind power generation. In this paper, considering the influence of different parameters on algorithm training and prediction, an improved moth flame optimization algorithm is constructed to optimize the LSTM wind energy prediction system to obtain better performance. The system consists of three modules: data preprocessing, optimization, and prediction. The data preprocessing module uses fuzzy information granulation to blur the input data. On this basis, the combination of swarm intelligent optimization algorithm and prediction model can effectively predict wind speed time series. Taking the California wind farm as an example, the MAPE of the experiment in the short-term forecast is 3.15%, the MAPE of the medium-term forecast is 4.38%, and the MAPE of the long-term forecast is 18.28%. The experimental results show that the proposed model has obvious advantages over the previous model.

In order to deal with the new threat of low altitude slow small (LSS) targets in air defense operations and provide support for LSS target interception decision, we propose a simple and reliable LSS target threat assessment method. Based on the detection capability of LSS targets and their threat characteristics, this paper proposes a threat evaluation factor and threat degree quantization function in line with the characteristics of LSS targets. LSS targets not only have the same threat characteristics as traditional air targets but also have the unique characteristics of flexible mobility and dynamic mission planning. Therefore, we use analytic hierarchy process (AHP) and information entropy to determine the subjective and objective threat factor weights of LSS targets and use the optimization model to combine them to obtain more reliable evaluation weights. Finally, the effectiveness and credibility of the proposed method are verified by experimental simulation.

Oil spectral analysis can effectively monitor the wear state of diesel engine, but the traditional gray prediction model has the disadvantages of insufficient effective information mining when large spectral data volume and not high prediction accuracy. To improve the accuracy of diesel wear prediction, this paper proposes a diesel engine wear prediction model based on the gray neural network model. The simulated values of the GM (1,1) model and the DGM(1,1) model were used as input to the neural network, and then the modeling prediction was performed. The case verification shows that the model is more accurate and closer to the actual value; fully the potential information of data, the accuracy and accuracy can be applied to the wear prediction of diesel engine.

In the complex and changeable air combat environment, how to accurately assess the threat of incoming targets is the key factor to determine the success or failure of air defense operations. Therefore, a threat assessment method based on dynamic relative entropy ranking method is proposed in this paper. Firstly, the attribute factors of air target are selected, and the target attribute matrix at different times is determined by intuitionistic fuzzy sets. Secondly, the inverse Poisson distribution method is used to determine the weight of time series, and then the time-weighted target attribute matrix is determined, and the target attribute is weighted by intuitionistic fuzzy entropy, so that the attribute-weighted intuitionistic fuzzy decision matrix is obtained. Thirdly, the relative entropy theory is introduced to improve the TOPSIS method, and the relative entropy ranking method is obtained. Then, a threat assessment model based on dynamic relative entropy ranking method is constructed, and the dynamic assessment of incoming targets is completed. Finally, the method is proved to be reasonable and effective by example analysis and comparison.

As the move towards Grid Integrated-Photovoltaic (GI-PV) system is proposed to improve the power quality development. A novel Adaptive Neuro-Fuzzy Inference System (ANFIS) based on improved Moth Flame Optimization (MFO) algorithm is described for grid integrated approach. The solar integration of Maximum Power Point (MPP) fed into modified Switched Boost Inverter (SBI) is presented, this GI-PV connected circuit has become prominent research in a recent scenario for energy demand. Proposed MFOA-ANFIS controller has generated the duty cycle pulses to each converter circuit. The benefit of grid-tied SBI is direct control outer-loop employed to obtain MFO-ANFIS techniques. To maintain a constant voltage DC-link is employed for inner-loop, this presence of constant DC-power to grid loads with support of MFO-ANFIS assists Proportional Integral Differential (PID) method. The results acquired by the simulation expressed that the proposed controller is addressed to maintain active and reactive power exchange, regulate DC bus-link voltages, grid voltage, and grid current. The effectiveness of the practical implication research is achieved by the output as represented as minimum grid harmonics, load current, and compensator current as verified in MATLAB/Simulink platform.

In this study, a GM(1,1) model is applied to forecast the trend of textile fashion colors. Through the historical time series data of the color suggestion ratios by international forecasting facilities in the past, the GM(1,1) model is applied to forecast the fashion color trend. Besides the GM(1,1) model, a gray neural network model (i.e. GNNM(1,1)) is developed to compare the precision with it as well. The simulated results show that the GM(1,1) model has higher forecast precision than the GNNM(1,1) one. The predicted results of the color trend using the GNNM(1,1) model are less close to the real circumstances than as expected. With the assistance of a gray model-based prediction model, the fashion color trend can be traced more precisely and easily.