Optimization of jamming formation of USV offboard active decoy clusters based on an improved PSO algorithm

Offboard active decoys(OADs)can effectively jam monopulse radars.However,for missiles approaching from a particular direction and distance,the OAD should be placed at a specific location,posing high requirements for timing and deployment.To improve the response speed and jamming effect,a cluster of OADs based on an unmanned surface vehicle(USV)is proposed.The formation of the cluster determines the effectiveness of jamming.First,based on the mechanism of OAD jamming,critical conditions are identified,and a method for assessing the jamming effect is proposed.Then,for the optimization of the cluster formation,a mathematical model is built,and a multi-tribe adaptive particle swarm optimization algorithm based on mutation strategy and Metropolis criterion(3M-APSO)is designed.Finally,the formation optimization problem is solved and analyzed using the 3M-APSO algorithm under specific scenarios.The results show that the improved algorithm has a faster convergence rate and superior performance as compared to the standard Adaptive-PSO algorithm.Compared with a single OAD,the optimal formation of USV-OAD cluster effectively fills the blind area and maximizes the use of jamming resources.

Optimization of buckling load for laminated composite plates using adaptive Kriging-improved PSO:A novel hybrid intelligent method

An effective hybrid optimization method is proposed by integrating an adaptive Kriging(A-Kriging)into an improved partial swarm optimization algorithm(IPSO)to give a so-called A-Kriging-IPSO for maximizing the buckling load of laminated composite plates(LCPs)under uniaxial and biaxial compressions.In this method,a novel iterative adaptive Kriging model,which is structured using two training sample sets as active and adaptive points,is utilized to directly predict the buckling load of the LCPs and to improve the efficiency of the optimization process.The active points are selected from the initial data set while the adaptive points are generated using the radial random-based convex samples.The cell-based smoothed discrete shear gap method(CS-DSG3)is employed to analyze the buckling behavior of the LCPs to provide the response of adaptive and input data sets.The buckling load of the LCPs is maximized by utilizing the IPSO algorithm.To demonstrate the efficiency and accuracy of the proposed methodology,the LCPs with different layers(2,3,4,and 10 layers),boundary conditions,aspect ratios and load patterns(biaxial and uniaxial loads)are investigated.The results obtained by proposed method are in good agreement with the literature results,but with less computational burden.By applying adaptive radial Kriging model,the accurate optimal resultsebased predictions of the buckling load are obtained for the studied LCPs.

A Novel Tuning Method for Predictive Control of VAV Air Conditioning System Based on Machine Learning and Improved PSO

The variable air volume(VAV)air conditioning system is with strong coupling and large time delay,for which model predictive control(MPC)is normally used to pursue performance improvement.Aiming at the difficulty of the parameter selection of VAV MPC controller which is difficult to make the system have a desired response,a novel tuning method based on machine learning and improved particle swarm optimization(PSO)is proposed.In this method,the relationship between MPC controller parameters and time domain performance indices is established via machine learning.Then the PSO is used to optimize MPC controller parameters to get better performance in terms of time domain indices.In addition,the PSO algorithm is further modified under the principle of population attenuation and event triggering to tune parameters of MPC and reduce the computation time of tuning method.Finally,the effectiveness of the proposed method is validated via a hardware-in-the-loop VAV system.

Coal mine safety production forewarning based on improved BP neural network

Firstly, the early warning index system of coal mine safety production was given from four aspects as per- sonnel, environment, equipment and management. Then, improvement measures which are additional momentum method, adaptive learning rate, particle swarm optimization algorithm, variable weight method and asynchronous learning factor, are used to optimize BP neural network models. Further, the models are applied to a comparative study on coal mine safety warning instance. Results show that the identification precision of MPSO-BP network model is higher than GBP and PSO-BP model, and MPSO- BP model can not only effectively reduce the possibility of the network falling into a local minimum point, but also has fast convergence and high precision, which will provide the scientific basis for the forewarnin~ management of coal mine safetv production.

Multi-path routing algorithm in WSN using an improvedparticle swarm optimization

To slove the problems of constrained energy and unbalanced load of wireless sensor network(WSN)nodes,a multipath load balancing routing algorithm based on neighborhood subspace cooperation is proposed.The algorithm adopts the improved particle swarm optimization(PSO)algorithm,takes the shortest distance and minimum energy consumption as optimization target and divides the nodes in one-hop neighborhood near the base station area into different regions.Furthermore,the algorithm designs a fitness function to find the best node in each region as a relay node and forward the data in parallel through the different paths of the relay nodes.The simulation results show that the proposed algorithm can reduce energy consumption and average end-to-end delay,balance network load and prolong network lifetime effectively.

Application of Improved PSO-LSSVM on Network Threat Detection

To solve the problem of the design of classifier in network threat detection, we conduct a simulation experiment for the parameters’ optimal on least squares support vector machine （LSSVM） using the classic PSO algorithm, and the experiment shows that uneven distribution of the initial particle swarm exerts a great impact on the results of LSSVM algorithm’s classification. This article proposes an improved PSO-LSSVM algorithm based on Divide-and-Conquer （DCPSO- LSSVM） to split the optimal domain where the parameters of LSSVM are in. It can achieve the purpose of distributing the initial particles uniformly. And using the idea of Divide-and-Conquer, it can split a big problem into multiple sub-problems, thus, completing problems’ modularization Meanwhile, this paper introduces variation factors to make the particles escape from the local optimum. The results of experiment prove that DCPSO-LSSVM has better effect on classification of network threat detection compared with SVM and classic PSOLSSVM.

Multi-objective reconfigurable production line scheduling for smart home appliances

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 local 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.

Equivalent model of multi-type distributed generators under faults with fast-iterative calculation method based on improved PSO algorithm

There are various types of distributed generators (DGs) with different grid integration strategies. The transient characteristics of the fault currents provided by the DGs are different to those of conventional synchronous generators. In this paper, a distribution network with multi-type DGs is investigated, including consideration of DG low-voltage ride through (LVRT). The fault current characteristics of two typical DGs, i.e. an inverter-interfaced distributed generator (IIDG) and a doubly-fed induction generator (DFIG), are analyzed, considering the specific operation modes. Based on analysis of the fault characteristics, an equivalent model of the multi-type DGs under symmetrical/asymmetrical fault conditions is established. A fast-iterative fault calculation method for enhancing the calculation efficiency while avoiding local convergence is then proposed using an improved particle swarm optimization (PSO) algorithm. A simulation system of the distribution network with multi-type DGs is established in PSCAD/EMTDC. The simulation results validate the high accuracy and calculation efficiency of the proposed calculation method of the fault components. This can assist in the settings of the protection threshold.

Using improved particle swarm optimization to tune PID controllers in cooperative collision avoidance systems

The introduction ofproportional-integral-dorivative （PID） controllers into cooperative collision avoidance systems （CCASs） has been hindered by difficulties in their optimization and by a lack of study of their effects on vehicle driving stability, comfort, and fuel economy. In this paper, we propose a method to optimize PID controllers using an improved particle swarm optimization （PSO） algorithm, and to bettor manipulate cooperative collision avoidance with other vehicles. First, we use PRESCAN and MATLAB/Simulink to conduct a united simulation, which constructs a CCAS composed of a PID controller, maneuver strategy judging modules, and a path planning module. Then we apply the improved PSO algorithm to optimize the PID controller based on the dynamic vehicle data obtained. Finally, we perform a simulation test of performance before and after the optimization of the PID controller, in which vehicles equipped with a CCAS undertake deceleration driving and steering under the two states of low speed （≤50 km/h） and high speed （≥100 km/h） cruising. The results show that the PID controller optimized using the proposed method can achieve not only the basic functions of a CCAS, but also improvements in vehicle dynamic stability, riding comfort, and fuel economy.

Research on the Optimization Approach for Cargo Oil Tank Design Based on the Improved Particle Swarm Optimization Algorithm

Based on the improved particle swarm optimization(PSO) algorithm,an optimization approach for the cargo oil tank design(COTD) is presented in this paper.The purpose is to design an optimal overall dimension of the cargo oil tank(COT) under various kinds of constraints in the preliminary design stage.A non-linear programming model is built to simulate the optimization design,in which the requirements and rules for COTD are used as the constraints.Considering the distance between the inner shell and hull,a fuzzy constraint is used to express the feasibility degree of the double-hull configuration.In terms of the characteristic of COTD,the PSO algorithm is improved to solve this problem.A bivariate extremum strategy is presented to deal with the fuzzy constraint,by which the maximum and minimum cargo capacities are obtained simultaneously.Finally,the simulation demonstrates the feasibility and effectiveness of the proposed approach.

Flight control for air-breathing hypersonic vehicles using linear quadratic regulator design based on stochastic robustness analysis

The flight dynamics model of air-breathing hypersonic vehicles （AHVs） is highly nonlinear and multivariable cou- pling, and includes inertial uncertainties and external disturbances that require strong, robust, and high-accuracy controllers. In this paper, we propose a linear-quadratic regulator （LQR） design method based on stochastic robustness analysis for the longitudinal dynamics of AHVs. First, input/output feedback linearization is used to design LQRs. Second, subject to various system parameter uncertainties, system robustness is characterized by the probability of stability and desired performance. Then, the mapping rela- tionship between system robustness and LQR parameters is established. Particularly, to maximize system robustness, a novel hybrid particle swarm optimization algorithm is proposed to search for the optimal LQR parameters. During the search iteration, a Chernoff bound algorithm is applied to determine the finite sample size of Monte Carlo evaluation with the given prohabilily levels. Finally, simulation results show that the optimization algorithm can effectively find the optimal solution to the LQR parameters.