Research on the Scheduling Problem of Movie Scenes by Yulian Liu, Qiuji Sun, Xiaotian Zhang, Yiwei Wu

“…A tabu search based method (TSBM) and a particle swarm optimization based method (PSOBM) are designed to solve larger-scale problems. …”
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Design of the End-effector of Waste Lithium-ion Battery Handling Robots by Zhang Hongsheng, Deng Ze

“…The rod length and position parameters of six-bar mechanism are optimized by genetic algorithm-BFGS （GA-BFGS） quasi-Newton method and particle swarm optimization-BFGS (PSO-BFGS) quasi-Newton method, and the dynamics model of the six-bar mechanism is established. …”
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The Hybrid Method of VMD-PSR-SVD and Improved Binary PSO-KNN for Fault Diagnosis of Bearing by Sheng-wei Fei

“…Fault diagnosis of bearing based on variational mode decomposition (VMD)-phase space reconstruction (PSR)-singular value decomposition (SVD) and improved binary particle swarm optimization (IBPSO)-K-nearest neighbor (KNN) which is abbreviated as VPS-IBPSOKNN is presented in this study, among which VMD-PSR-SVD (VPS) is presented to obtain the features of the bearing vibration signal (BVS), and IBPSO is presented to select the parameter K of KNN. …”
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Repairing the Inconsistent Fuzzy Preference Matrix Using Multiobjective PSO by Abba Suganda Girsang, Chun-Wei Tsai, Chu-Sing Yang

“…This paper presents a method using multiobjective particle swarm optimization (PSO) approach to improve the consistency matrix in analytic hierarchy process (AHP), called PSOMOF. …”
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Performance analysis of secrecy rate for SWIPT in massive antenna by Hui BAO, Minmin ZHANG, Yaqing YAO, Hui WANG

“…In the multi-drop broadcast system,the existing research is to optimize the single-user security rate in the complete channel state information.In fact,it’s impossible that there exists only one user in the system.The base station often receives incomplete channel state information.Aiming at this problem,a robust beamforming scheme was proposed.In the multi-user case,considering the influence of channel estimation error on the system security rate,the particle swarm optimization algorithm was used to optimize the emission beamforming vector,artificial noise covariance and power split ratio to ensure that the user collects a certain energy while maximizing the safe transmission rate.The simulation results show that the proposed scheme is slightly lower than the security rate in the ideal case,but it is meaningful to the actual system,taking into account the existence of eavesdropping users and the estimation error.…”
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An Improved PSO Algorithm for Distributed Localization in Wireless Sensor Networks by Dan Li, Xian bin Wen

“…Accurate and quick localization of randomly deployed nodes is required by many applications in wireless sensor networks and always formulated as a multidimensional optimization problem. Particle swarm optimization (PSO) is feasible for the localization problem because of its quick convergence and moderate demand for computing resources. …”
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Application of Golden Sine Algorithm in Hydrogeological Parameter Optimization by ZHOU Yourong, LI Na, ZHOU Fahui

“…To improve the accuracy of hydrogeological parameters, this paper conducts the simulationverification of golden sine algorithm (Gold-SA) algorithm by six standard test functions andcompares the simulation results with that of the particle swarm optimization (PSO) algorithm, andtaking two pumping test data as examples, optimizes two key parameters, i.e. transmissivitycoefficient and storage coefficient of theis formula by the Gold-SA algorithm and compares theresults with that of the PSO algorithm, wiring method and literature method. …”
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Predicting Shear Strength in FRP-Reinforced Concrete Beams Using Bat Algorithm-Based Artificial Neural Network by Mohammad Nikoo, Babak Aminnejad, Alireza Lork

“…ANN is also compared to the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithm. Finally, Nehdi et al.’s model, ACI-440, and BISE-99 equations were used to evaluate the models’ accuracy. …”
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Parameter Optimization of Single-Diode Model of Photovoltaic Cell Using Memetic Algorithm by Yourim Yoon, Zong Woo Geem

“…First, 10 single algorithms were considered including genetic algorithm, simulated annealing, particle swarm optimization, harmony search, differential evolution, cuckoo search, least squares method, and pattern search; then their final solutions were used as initial vectors for generalized reduced gradient technique. …”
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MULTI-OBJECTIVE OPTIMIZATION DESIGN OF MW WIND TURBINE AIRFOILS by LI YingJue, WEI KeXiang, ZHOU Zhou

“…Designed to improve the aerodynamic performance of the blade and reduce noise,this paper aims to improve the aerodynamic performance of the blade and reduce the noise,optimize the airfoil structural equation based on the improved Hicks-Henne function,and combine the multi-objective particle swarm optimization algorithm to optimize the design of the S8035 airfoil. …”
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RESEARCH ON ROLLING BEARING FAULT DIAGNOSIS BASED ON MA OPTIMIZATION OF CNN by TIAN LiYong, ZHAO JianJun, YU Ning

“…Aiming at the high dependence of super parameter selection on artificial experience in rolling bearing state identification based on convolution neural network(CNN),a fault diagnosis model(CNN⁃MA)based on mayfly algorithm(MA)was proposed.Firstly,the model used the powerful optimization ability of MA,took the diagnostic accuracy of CNN as the optimization objective,and adaptively adjusted the super parameters in CNN.Secondly,the normalized original signal image set was used to preserve the characteristics of the signal as much as possible.Finally,in order to evaluate the effectiveness of the parameters in the optimization model,compared with the CNN model optimized by particle swarm optimization(PSO)algorithm.The results show that the proposed model has more stable performance,higher recognition accuracy and good anti⁃noise ability.It fully shows the feasibility and reliability of CNN⁃MA model in fault diagnosis of rolling bearings.…”
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Distributed hybrid energy storage photovoltaic microgrid control based on MPPT algorithm and equilibrium control strategy by Yanlong Qi, Rui Liu, Haisheng Lin, Junchen Zhong, Zhen Chen

“…The novelty of this study is that the improved Grey Wolf optimization algorithm enhances the global search ability by introducing the random jump mechanism of Levy flight algorithm and the combination of particle swarm optimization algorithm and Grey Wolf optimization algorithm to avoid falling into the local optimal. …”
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Supply Capability Evaluation of Intelligent Manufacturing Enterprises Based on Improved BP Neural Network by Quan Quan, Zhongqiang Zhang

“…In this paper, based on the traditional backpropagation (BP) neural network, combined with the improved particle swarm optimization (PSO) algorithm, and on the basis of the supplier evaluation index system, the supplier efficiency evaluation model of intelligent manufacturing enterprises based on DPMPSO-BP neural network is constructed. …”
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Prediction method of soil water content based on SVM optimized by improved salp swarm algorithm by Xiaoqiang ZHAO, Fan YANG, Zhufeng YAN

“…Aiming at the problems of low accuracy and low efficiency of traditional soil water content prediction methods, support vector machine (SVM) was used to establish a prediction model, and the soil water content prediction method based on SVM optimized was proposed by the improved salp swarm algorithm.Firstly, the opposition-based learning and chaotic optimization were introduced to improve the standard salp swarm algorithm to solve the problem that the algorithm was easy to fall into the local optimal solution and its convergence speed was slow.Secondly, the improved salp swarm algorithm was used to optimize the parameters that affect the performance of SVM and the corresponding prediction model was built.Finally, the proposed model was compared with the particle swarm optimization SVM and the whale algorithm optimized SVM prediction model.The experimental results show that the mean square error and decision coefficient of the proposed model are 0.42 and 0.901, which are better than the other two models which verified the effectiveness of the proposed method.…”
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Wireless sensor node localization based on IPSO-MC by Yongyan LI, Jianping WU

“…To solve the problem of insufficient node positioning accuracy in wireless sensor networks,an algorithm based on improved particle swarm optimization by membrane computing (IPSO-MC) was proposed.Kent mapping was used to initialize the population and domain particles were introduced to improve the global optimization of the particle swarm.The weight factor and nonlinear extreme value perturbation were used to improve the local optimization ability of the particle swarm,and the Levy flight mechanism was used to optimize the individual position.Finally,the optimal solution of the particle swarm algorithm was obtained by the evolutionary rules of the membrane computing.Simulation experiments show that compared with the chicken flock algorithm,the improved particle swarm algorithm and the membrane computing,the proposed algorithm improves 3.24%,5.12% and 8.15% in the comparison of reference node ratio indicators,and the increase in the number of nodes indicators by 2.26%,7.82% and 9.81%,and the comparison of communication radius indicators increased by 2.15%,5.5% and 7.5%,respectively.This indicates that the algorithm has a good effect in node localization.…”
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Deep reinforcement learning-based resource joint optimization for millimeter-wave massive MIMO systems by LIU Qingli, LI Xiaoyu, LI Rui

“…Experimental results show that the proposed joint optimization method significantly improves the throughput and energy efficiency of the system compared with the single-stage all-digital precoding and hybrid precoding equal resource allocation methods and the particle swarm optimization-based resource allocation algorithm.…”
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Timing evolution and prediction of Internet transmission behavior by He TIAN, Hai ZHAO, Jinfa WANG, Chuan LIN

“…The transmission behavior of Internet plays an importance role in the research on the relationship between network topology structure and dynamic behavior.Selecting effective path samples in four monitoring points which from different regions authorized by CAIDA_Ark project and statistics network traveling time and traveling diameter,their correlation is very weak,network traveling time is presented on multi-peak and heavy tail distribution.Using nonlinear time sequences analysis method to identify the Chaos characteristics of network traveling time evolution sequences.The results show that their timing evolution has Chaos characteristics.Based on this,the Logistic equation was lead to establish network transmission behavior prediction model,and particle swarm optimization (PSO) was used to optimize model parameters.The model by the network traveling time sequences of four monitoring points was experimented,evaluated it from accuracy and availability,the results show that the model can predict network transmission behavior accurately in the short term.It can be used as a tool for predicting the network behaviors’ evolution in a period of time.…”
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Optimization in Construction Management Using Adaptive Opposition Slime Mould Algorithm by Pham Vu Hong Son, Luu Ngoc Quynh Khoi

“…In order to compare AOSMA with a nondominated sorting genetic algorithm III (NSGA III), multiobjective particle swarm optimization (MOPSO), LHS-based NSGA III, and a hybrid model of MAWA (MAWA-TLBO, MAWA-GA, MAWA-AS, and MAWA-ACS-SGPU) and to assess the model’s potential and viability, performance evaluation indexes are applied. …”
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Optimum Design of truss structures considering nonlinear analysis and dynamic loading using metaheuristic algorithms by Bárbara Scardini Domingues, Marcos Antônio Campos Rodrigues, Élcio Cassimiro Alves

“…In this research, the authors investigated the optimization of trusses performing a geometric nonlinear analysis under dynamic loading, using two different metaheuristic algorithms: the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The objective function was to minimize the weight of the structure. …”
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An integrated optimization model of network behavior victimization identification based on association rule feature extraction by Shengli ZHOU, Linqi RUAN, Rui XU, Xikang ZHANG, Quanzhe ZHAO, Yuanbo LIAN

“…The identification of the risk of network behavior victimization was of great significance for the prevention and warning of telecom network fraud.Insufficient mining of network behavior features and difficulty in determining relationships, an integrated optimization model for network behavior victimization identification based on association rule feature extraction was proposed.The interactive traffic data packets generated when users accessed websites were captured by the model, and the implicit and explicit behavior features in network traffic were extracted.Then, the association rules between features were mined, and the feature sequences were reconstructed using the FP-Growth algorithm.Finally, an analysis model of telecom network fraud victimization based on network traffic analysis was established, combined with the stochastic forest algorithm of particle swarm optimization.The experiments show that compared with general binary classification models, the proposed model has better precision and recall rates and can effectively improve the accuracy of network fraud victimization identification.…”
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