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A novel ensemble learning method using multiple objective particle swarm optimization for subject-independent EEG-based emotion recognition

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COMPUTERS IN BIOLOGY AND MEDICINE 2022年 140卷 105080-105080页

作者： Li, Rui Ren, Chao Zhang, Xiaowei Hu, Bin Lanzhou Univ Sch Informat Sci & Engn Gansu Prov Key Lab Wearable Comp Lanzhou Peoples R China

Emotion recognition is a vital but challenging step in creating passive brain-computer interface applications. In recent years, many studies on electroencephalogram (EEG)-based emotion recognition have been conducted. Ensemble learning has been widely used in emotion recognition because of its superior accuracy and generalization. In this study, we proposed a novel ensemble learning method based on multiple objective particle swarm optimization for subject-independent EEG-based emotion recognition. First, we used a 4 s sliding time window with a 2 s overlap to extract 13 different features from EEG signals and construct a feature vector. Then, we employed L1 regularization to select effective features. Second, a model selection method was applied to choose the optimal basic analysis submodels. Afterward, we proposed an ensemble operator that converts the classification results of a single model from discrete values to continuous values to better characterize the classification results. Subsequently, multiple objective particle swarm optimization was adopted to confirm the optimal parameters of the ensemble learning model. Finally, we conducted extensive experiments on two public datasets: DEAP and SEED. Considering the generalization of the model, we applied leave-one-subject-out cross-validation to evaluate the performance of the model. The experimental results demonstrate that the proposed method achieves a better recognition performance than single methods, commonly used ensemble learning methods, and state-of-the-art methods. The average accuracies for arousal and valence are 65.70% and 64.22%, respectively, on the DEAP database, and the average accuracy on the SEED database is 84.44%.

关键词： Emotion recognition EEG multiple objective particle swarm optimization Ensemble learning

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A particle swarm optimization for a fuzzy multi-objective unrelated parallel machines scheduling problem

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APPLIED SOFT COMPUTING 2013年第12期13卷 4750-4762页

作者： Torabi, S. A. Sahebjamnia, N. Mansouri, S. A. Bajestani, M. Aramon Univ Tehran Sch Ind Engn Coll Engn Tehran Iran Brunel Univ Brunel Business Sch Uxbridge UB8 3PH Middx England

This paper proposes a novel multi-objective model for an unrelated parallel machine scheduling problem considering inherent uncertainty in processing times and due dates. The problem is characterized by non-zero ready times, sequence and machine-dependent setup times, and secondary resource constraints for jobs. Each job can be processed only if its required machine and secondary resource (if any) are available at the same time. Finding optimal solution for this complex problem in a reasonable time using exact optimization tools is prohibitive. This paper presents an effective multi-objective particle swarm optimization (MOPSO) algorithm to find a good approximation of Pareto frontier where total weighted flow time, total weighted tardiness, and total machine load variation are to be minimized simultaneously. The proposed MOPSO exploits new selection regimes for preserving global as well as personal best solutions. Moreover, a generalized dominance concept in a fuzzy environment is employed to find locally Pareto-optimal frontier. Performance of the proposed MOPSO is compared against a conventional multiobjective particle swarm optimization (CMOPSO) algorithm over a number of randomly generated test problems. Statistical analyses based on the effect of each algorithm on each objective space show that the proposed MOPSO outperforms the CMOPSO in terms of quality, diversity and spacing metrics. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Fuzzy mathematical programming Unrelated parallel machine scheduling Secondary resource constraints multiple objective particle swarm optimization

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Artificial Intelligence for Wind Speed Forecasting: A Review on Multi-Scale Decomposition and Intelligent Fusion Strategies

Advances in Wind Engineering

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Advances in Wind Engineering 2025年

作者： Hui Liu Rui Yang Institute of Artificial Intelligence and Robotics (IAIR) Key Laboratory of Traffic Safety on Track of Ministry of Education School of Traffic and Transportation Engineering Central South University Changsha 410075 Hunan China

Accurate and reliable short-term wind speed forecasting is essential for the effective utilization and scientific dispatch of wind energy. In recent years, with the rapid development of Artificial Intelligence (AI) technology, the capabilities of prediction models have been significantly improved. However, there are still many challenges in dealing with non-stationary signals, coordinating multi-objective conflicts, and enhancing model robustness. This paper reviews the three core links in wind speed forecasting: multi-scale signal decomposition, hybrid modeling framework, and multi-objective optimization algorithm. First, this paper systematically summarizes common signal decomposition methods, including Empirical Mode Decomposition (EMD), Variational Mode Decomposition (VMD), and wavelet transform-based techniques, and analyzes the characteristics of these methods in terms of frequency resolution, adaptability, and model ensemble capabilities. Secondly, from the perspective of structural form and functional complementarity, this paper explores how to combine shallow models, deep neural networks, and ensemble strategies to build an efficient hybrid prediction model. Finally, a variety of Multi-objective optimization (MOO) algorithms are evaluated, such as the Non-Dominated Sorting Genetic Algorithm II (NSGA-II), the Multi-objective particle swarm optimization Algorithm (MOPSO), and the Strength Pareto Evolutionary Algorithm II (SPEA-II). Their potential in model architecture adjustment, fusion strategy design, and balancing prediction accuracy, stability, and computational efficiency is analyzed. This paper comprehensively evaluates related studies according to the "decomposition-prediction-optimization" process. At the same time, it emphasizes the importance of developing a wind speed prediction system that is robust, interpretable, and easy to deploy. This study provides a unified perspective and reference framework for building intelligent and goal-oriented wind

关键词： ADMM Alternating Direction Method of Multipliers AI Artificial Intelligence ANN Artificial Neural Networks AutoML Automated Machine Learning AVMD Adaptive Variational Mode Decomposition BEMD Bivariate or Complex Empirical Mode Decomposition CEEMD Complementary versions such as Complete Ensemble Empirical Mode Decomposition CEEMDAN Complete Ensemble Empirical Mode Decomposition with Adaptive Noise CFD Computational Fluid Dynamics CNN Convolutional Neural Networks CWT Continuous Wavelet Transform DFT Discrete Fourier Transform DRL Deep Reinforcement Learning ECMWF European Centre for Medium-Range Weather Forecasts EEMD Ensemble Empirical Mode Decomposition ELM Extreme Learning Machines EMD Empirical Mode Decomposition ESN Echo State Network FFT Fast Fourier Transform GDE3 Generalized Differential Evolution 3 GNNs Graph Neural Networks GRU Gated Recurrent Unit LES Large Eddy Simulation IMFs Intrinsic Mode Functions LightGBM Light Gradient Boosting Machine LSTM Long Short-Term Memory MAE Mean Absolute Error MEMD Multivariate Empirical Mode Decomposition MODWT Maximal Overlap Discrete Wavelet Transform ε -MOEA Epsilon Multi-objective Evolutionary Algorithm MOEA/D Multi-objective Evolutionary Algorithm based on Decomposition MOO Multi-objective optimization MOPSO multiple objective particle swarm optimization MRVMD Multi-resolution Variational Mode Decomposition MVMD Multivariate Variational Mode Decomposition NSGA-II Non-Dominated Sorting Genetic Algorithm II NWP Numerical Weather Prediction PAES Pareto Archived Evolution Strategy PINNs Physics-Informed Neural Networks RANS Reynolds-Averaged Navier–Stokes RL Reinforcement Learning RMSE Root Mean Square Error SPEA-II Strength Pareto Evolutionary Algorithm II SSA Singular Spectrum Analysis SVD Singular Value Decomposition SVM Support Vector Machines SVR Support Vector Regression VMD Variational Mode Decomposition WD, Wav elet Decomposition WRF Weather Research and Forecasting WT Wavelet Transform XGBoost eXtreme Gradient Boosting Artificial Intelligence Wind speed forec

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A cluster positioning architecture and relative positioning algorithm based on pigeon fock bionics

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Satellite Navigation 2023年第1期4卷 92-112,I0004页

作者： Zhongliang Deng Hang Qi Chengfeng Wu Enwen Hu Runmin Wang Beijing University of Posts and Telecommunications Beijing 100876China Science and Technology on Complex System Control and Intelligent Agent Cooperative Laboratory Beijing 100074China.

Unmanned clusters can realize collaborative work,fexible confguration,and efcient operation,which has become an important development trend of unmanned *** positioning is important for ensuring the normal operation of unmanned *** existing solutions have some problems such as requiring external system assistance,high system complexity,poor architecture scalability,and accumulation of positioning errors over *** the aid of the information outside the cluster,we plan to construct the relative position relationship with north alignment to adopt formation control and achieve robust cluster relative *** on the idea of bionics,this paper proposes a cluster robust hierarchical positioning architecture by analyzing the autonomous behavior of pigeon *** divide the clusters into follower clusters,core clusters,and leader nodes,which can realize fexible networking and cluster *** at the core cluster that is the most critical to relative positioning in the architecture,we propose a cluster relative positioning algorithm based on spatiotemporal correlation *** the design idea of low cost and large-scale application,the algorithm uses intra-cluster ranging and the inertial navigation motion vector to construct the positioning equation and solves it through the Multidimensional Scaling(MDS)and multiple objective particle swarm optimization(MOPSO)*** cluster formation is abstracted as a mixed direction-distance graph and the graph rigidity theory is used to analyze localizability conditions of the *** designed the cluster positioning simulation software and conducted localizability tests and positioning accuracy tests in diferent *** with the relative positioning algorithm based on Extended Kalman Filter(EKF),the algorithm proposed in this paper has more relaxed positioning conditions and can adapt to a variety of *** also has higher relative positioning accuracy

关键词： Cluster positioning architecture Cluster relative positioning Multidimensional scaling multiple objective particle swarm optimization Unmanned aerial vehicles positioning Localizability analysis Rigid graph

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Design of the PID Controller for Hydro-turbines Based on optimization Algorithms

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INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2020年第7期18卷 1758-1770页

作者： Perng, Jau-Woei Kuo, Yi-Chang Lu, Kuan-Chung Natl Sun Yat Sen Univ Dept Mech & Electromech Engn 70 Lienhai Rd Kaohsiung 80424 Taiwan

In this study, multiple objective particle swarm optimization (MOPSO), genetic algorithm, bees, and reinforcement learning (RL) are used to calculate the rise time (tr), integral square-error, integral of time-multiplied-squared-error, integral absolute error, and integral of time multiplied by absolute error of the system transfer function and then we use a fuzzy algorithm on MOPSO, GA, bees, and RL based on the frequency sensitivity margin of a water turbine governor to optimize the proportional gain (kp) and integral gain (ki) and calculate the relative collapsing frequency response values. The MOPSO algorithm returned the optimal result. The radial basis function (RBF) neural network curve is obtained from the MOPSO algorithm with three variables (i.e., kp, ki, kd = 0.6 and grid frequency deviations values), and finally we identify and predict three variable values near the RBF neural network curve through deep learning. The result of the grid frequency deviation is close to 0, and the gain response time is better for damping the frequency oscillations in different operating conditions.

关键词： Bees deep learning frequency sensitivity genetic algorithm integral absolute error integral gain integral of time multiplied by absolute error integral of time-multiplied-squared-error integral square-error multiple objective particle swarm optimization neural network proportional gain radial basis function reinforcement learning rise time

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Multi - focus image fusion using self - resemblance measure

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COMPUTERS & ELECTRICAL ENGINEERING 2018年 71卷 13-27页

作者： Paramanandham, Nirmala Rajendiran, Kishore SSN Coll Engn Dept Elect & Commun Engn Chennai Tamil Nadu India

Image fusion is the process of integrating several source images into a single image that provides more reliable information along with reduced redundancy. Inspired by the pixel correlation property, a fusion rule based on self-resemblance measure (SRM) is proposed. An efficient image fusion algorithm is presented for fusing multi-focus images using self resemblance measure, consistency verification and multiple objective particle swarm optimization. Initially based on SRM fusion rule, the source images are fused and then the block sizes are adaptively selected by particle swarm optimization based on the multiple objective fitness functions for obtaining enhanced fused image. The proposed framework is evaluated using quantitative metrics such as root mean square error, peak signal to noise ratio, correlation, standard deviation, mutual information and Petrovic metric. Experimental results demonstrate the outperformance of the proposed algorithm over many other well known state-of-the-art fusion techniques, both in visual effect and objective evaluation criteria.

关键词： Block separation Complexity Image fusion multiple objective particle swarm optimization Self resemblance measure

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A multiple objective decision making model for energy generation portfolio under fuzzy uncertainty: Case study of large scale investor-owned utilities in Florida

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RENEWABLE ENERGY 2015年 75卷 224-242页

作者： Zeng, Ziqiang Nasri, Ehsan Chini, Abdol Ries, Robert Xu, Jiuping Sichuan Univ Uncertainty Decis Making Lab Chengdu 610064 Peoples R China Univ Florida Rinker Sch Construct Management Gainesville FL 32611 USA Univ Florida Coll Design Construct & Planning Gainesville FL 32611 USA

The objective of this paper is to present a methodology to evaluate the viability of developing solar photovoltaic projects for large investor-owned utilities. By taking into account the trade-off between the cost per kWh of electricity generation and total risk for an investor-owned utility, a multi-objective model of the energy generation portfolios is developed. The decision making model can determine the proportion of different energy generation sources in an investor-owned utility portfolio that reduces risk while providing the lowest cost per kWh of electricity generation possible. In order to measure the risk of the investor-owned utility for energy portfolio selection, an investigation of possible dangers and failures of energy generation portfolios is made and 9 main failure modes are identified. The failure mode and effects analysis is employed to calculate the risk priority numbers for each risk. To deal with the uncertainties of the levelized cost of electricity and risk levels of failure modes, the fuzzy method is introduced and an equivalent crisp model is derived which is then solved by employing a multiple objective particle swarm optimization algorithm. The analysis for four large scale investor-owned utilities in Florida is presented to highlight the performance of the developed optimization method. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Solar PV Energy generation portfolio Investor-owned utilities Failure mode and effects analysis multiple objective particle swarm optimization

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optimization of CNC Turning Machining Parameters Based on Bp-DWMOPSO Algorithm

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Computers, Materials & Continua 2023年第10期77卷 223-244页

作者： Jiang Li Jiutao Zhao Qinhui Liu Laizheng Zhu Jinyi Guo Weijiu Zhang College of Mechanical and Electrical Engineering Harbin Engineering UniversityHarbin150001China

Cutting parameters have a significant impact on the machining *** order to reduce the machining time and improve the machining quality,this paper proposes an optimization algorithm based on Bp neural networkImproved Multi-objective particle swarm(Bp-DWMOPSO).Firstly,this paper analyzes the existing problems in the traditional multi-objective particle swarm ***,the Bp neural network model and the dynamic weight multi-objective particle swarm algorithm model are ***,the Bp-DWMOPSO algorithm is designed based on the established *** order to verify the effectiveness of the algorithm,this paper obtains the required data through equal probability orthogonal experiments on a typical Computer Numerical Control(CNC)turning machining case and uses the Bp-DWMOPSO algorithm for *** experimental results show that the Cutting speed is 69.4 mm/min,the Feed speed is 0.05 mm/r,and the Depth of cut is 0.5 *** results show that the Bp-DWMOPSO algorithm can find the cutting parameters with a higher material removal rate and lower spindle load while ensuring the machining *** method provides a new idea for the optimization of turning machining parameters.

关键词： Machining parameters Bp neural network multiple objective particle swarm optimization Bp-DWMOPSO algorithm

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An economic-statistical design of simple linear profiles with multiple assignable causes using a combination of MOPSO and RSM

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SOFT COMPUTING 2021年第16期25卷 11087-11100页

作者： Ershadi, M. J. Ershadi, M. M. Haghighi Naeini, S. Niaki, S. T. A. Iranian Res Inst Informat Sci & Technol IranDoc Dept Informat Technol Enghelab Ave Felestin IntersectPOB 13185-1371 Tehran *** Iran Amirkabir Univ Technol Dept Ind Engn & Management Syst Tehran Iran Islamic Azad Univ Ind Engn Dept Sci & Res Branch Tehran Iran Sharif Univ Technol Dept Ind Engn Dept Tehran Iran

An economic-statistical design with multiple assignable causes following exponential distribution is presented in this paper for linear profiles. For this purpose, a tri-objective optimization model is proposed to minimize the cost with desired statistical performances. Average Run Length (ARL) as the primary statistical measure is employed for the appraisal of the designed linear profiles. The first objective to be minimized is a cost function that models the implementation cost in different states. The second objective is to maximize ARL(0) or the in-control average run-length of the monitoring scheme. The third objective to be minimized is ARL(1) or the out-of-control average run-length of the control chart. Besides, there are two constraints defined as the lower and the upper bounds on ARL(0) and ARL(1), respectively. This model optimizes the sample size, the sampling intervals, and related parameters for assignable causes concerning exponential distribution. As the problem is hard to solve analytically, a parameter-tuned meta-heuristic solution algorithm involving a multiple objective particle swarm optimization (MOPSO) alongside the response surface methodology (RSM) is employed to find a near-optimum solution. In this meta-heuristic, the response surface methodology is utilized to tune the parameters of the optimization problem. A numerical example is presented to illustrate the application of the proposed approach and assess its performance. Finally, sensitivity analyses of some parameters on the effects of the design parameters are performed. The results show the parameter profiling cost per set-points of a sample has the most effective implementation costs of linear profiles. Also, the proposed model is robust on some parameters such as the cost of identifying and modifying an assignable cause, fixed cost of sampling, and variable cost of the sampling process.

关键词： Linear profiles multiple assignable causes Economic-statistical design multiple objective particle swarm optimization Response surface methodology

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Set-point Determination and Regualtion for Flue Gas Desulphurization Process by ELM-assisted MOPSO and LQIR controller 41

Set-point Determination and Regualtion for Flue Gas Desulphu...

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第41届中国控制会议

作者： Quanbo Liu Xiaoli Li Kang Wang Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing Advanced Innovation Center for Future Internet TechnologyBeijing University of Technology Engineering Research Center of Digital Community Ministry of EducationBeijing University of Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In China,coal accounts for over a half of the total energy consumption and it is primarily used in coal-fired power ***,the change of a dominant role that coal played in electricity generation appears to be impossible in the near *** operation of coal-fired power stations involves combustion of coal,which would generate a vast amount of sulphur dioxide（SO）,the resulting SO in turn pose numbers of environmental and human-health *** view of the above considerations,optimization and control techiniques should be incorporated in flue gas desulphurization（FGD） system,so that further improvement in overall performance of FGD can be *** this paper,based upon historical operational data from FGD system at a 1000 MW unit coal-fired power station,we formulate the set-point determination in FGD process into a constrained many-objective optimization *** of all,multiple objective particle swarm optimization（MOPSO） algorithm along with extreme learning machine（ELM） is utilized to yield Pareto-optimal ***,for control purpose,setpoint is determined from the obtained Pareto ***,with the use of linear quadratic integral controller（LQIR）,the setpoint regualation task is *** simulation studies,derived respresentative point is theoretically justified and FGD process can be regulated to it successfully.

关键词： many-objective optimization flue Gas Desulphurization linear quadratic regulator multiple objective particle swarm optimization extreme learning machine

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