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An improved multi-objective particle swarm optimization and its application in raw ore dispatching

ADVANCES IN MECHANICAL ENGINEERING 2018年第2期10卷

作者： Zhang, Chao Li, Qing Chen, Peng Feng, Qian Cui, Jiarui Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China Minist Educ Key Lab Knowledge Automat Ind Proc Beijing Peoples R China

An improved multi-objective particle swarm optimization with time-varying parameter and follower bee search is proposed in this article. In this algorithm, the weight of personal best solution decreases gradually as the iteration continues. This approach eliminates the effect caused by its poorer quality compared to global best solution so that the convergence ability of the algorithm is improved. Furthermore, the follower bee search in artificial bee colony algorithm is introduced to strengthen the randomness of the algorithm and discover more non-dominated solutions. A comparative simulation study is carried out using internal raw ore dispatching in an iron mining group that contains multiple stopes and concentrating mills. The results show that the proposed algorithm can significantly improve convergence and diversity.

关键词： multi-objective particle swarm optimization time-varying parameter follower bee search raw ore dispatching

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Voltage Regulation for PV-integrated Power Grid using Improved multi-objective particle swarm optimization

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RECENT ADVANCES IN ELECTRICAL & ELECTRONIC ENGINEERING 2024年

作者： Kewen, Li Lei, Yu Qianyi, Chen Xinhao, Lin Shifeng, Ou Lyuzerui, Yuan Elect Power Sci Res Inst Guangxi Power Grid Co Ltd Dept Distribut Syst Nanning 530023 Guangxi Peoples R China Elect Power Res Inst China Southern Power Grid Elect Power Res Guangzhou 510630 Guangdong Peoples R China Elect Power Sci Res Inst Guangxi Power Grid Co Ltd Elect Power Res Nanning 530023 Guangxi Peoples R China Elect Power Res Inst China Southern Power Grid Elect Distribut Syst Res Guangzhou Peoples R China

Introduction With the widespread integration of distributed photovoltaics into the power grid, challenges such as voltage fluctuations and increased deviations have arisen, alongside heightened demands for power quality and economic *** This study tackles the complexities of integrating distributed photovoltaic systems into the power grid, encompassing network flow variations, node voltage fluctuations, and risks of exceeding limits while also considering scheduling costs. A comprehensive control strategy is proposed, integrating the coordinated output of reactive power adjustable devices, energy storage systems (ESS), and transformers. Subsequently, a multi-objective optimization mathematical model is formulated with the aims of minimizing network loss, operation cost, and voltage deviation. An enhanced multi-objective particle swarm optimization (MOPSO) algorithm is introduced, leveraging a ring neighborhood topology and specialized crowding distance measurement to establish a stable network in both decision and target spaces, devoid of any niche *** This approach eliminates subjective influences while ensuring rationality. Tests on the IEEE 30-node network example demonstrate that the algorithm can generate a large number of Pareto optimal solutions, highlighting its advantages in the application of reactive power *** The method proposed in this paper can fully utilize the dispatchable equipment in the power grid, effectively reducing network losses and voltage deviations while maintaining low operating costs.

关键词： Voltage Regulation multi-objective particle swarm optimization ring neighborhood topology distributed power supply reactive power optimization energy storage systems

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Dimension Reduction for Hyperspectral Remote Sensor Data Based on multi-objective particle swarm optimization Algorithm and Game Theory

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SENSORS 2019年第6期19卷 1327.-1327.页

作者： Gao, Hongmin Yang, Yao Zhang, Xiaoke Li, Chenming Yang, Qi Wang, Yongchang Hohai Univ Coll Comp & Informat Nanjing 211100 Jiangsu Peoples R China Hohai Univ Sch Publ Adm Nanjing 211100 Jiangsu Peoples R China

Information entropy and interclass separability are adopted as the evaluation criteria of dimension reduction for hyperspectral remote sensor data. However, it is rather single-faceted to simply use either information entropy or interclass separability as evaluation criteria, and will lead to a single-target problem. In this case, the chosen optimal band combination may be unfavorable for the improvement of follow-up classification accuracy. Thus, in this work, inter-band correlation is considered as the premise, and information entropy and interclass separability are synthesized as the evaluation criterion of dimension reduction. The multi-objective particle swarm optimization algorithm is easy to implement and characterized by rapid convergence. It is adopted to search for the optimal band combination. In addition, game theory is also introduced to dimension reduction to coordinate potential conflicts when both information entropy and interclass separability are used to search for the optimal band combination. Experimental results reveal that compared with the dimensionality reduction method, which only uses information entropy or Bhattacharyya distance as the evaluation criterion, and the method combining multiple criterions into one by weighting, the proposed method achieves global optimum more easily, and then obtains a better band combination and possess higher classification accuracy.

关键词： multi-objective particle swarm optimization game theory hyperspectral remote sensor data dimension reduction band selection

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Credible Web Service Composition based on Improved multi-objective particle swarm optimization

Credible Web Service Composition based on Improved Multi-obj...

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第40届中国控制会议

作者： Feng Liu Min Han Jianwei Liu Faculty of Electronic Information and Electrical Engineering Dalian University of Technology Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Faculty of Infrastructure Engineering Dalian University of Technology

In the field of Web service composition,since the published value of Quality of Service(QoS) is not necessarily reliable,the research on the credibility of Web services has become a research ***,the current credibility research cannot well describe the difference between the published value and the actual operating *** the same time,the algorithm used to solve the Web service composition scheme generally has the problem of poor solution *** on this,this paper proposes a credible Web service composition method based on improved multi-objective particle swarm optimization(IMOPSO).Firstly,this paper proposes a similarity-based credibility calculation model,and combines it with the QoS published value through the inverse of the similarity,so that the QoS of the Web service meets the actual operation ***,this paper proposes an improved multi-objective particle swarm optimization algorithm,which solves the problem of premature convergence by expanding the population diversity,and the solution effect is significantly ***,the improved multi-objective particle swarm algorithm is used to solve the Web service composition problem based on credible Qo *** experiments on the QWS2 data set and the Synthetic data set prove that the method proposed in this paper can effectively improve the credibility of the service composition scheme,and has higher composition efficiency and better composition effect.

关键词： credibility Web service composition QoS multi-objective particle swarm optimization

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A Feature Selection Algorithm to Intrusion Detection Based on Cloud model and multi- objective particle swarm optimization

A Feature Selection Algorithm to Intrusion Detection Based o...

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2011 Fourth International Symposium on Computational Interlligence and Design [第四届计算智能与设计国际会议 ISCID 2011]

作者： Liu-Hong Zhou Yan-Hua Liu Guo-Long Chen College of Mathematics and Computer Science Fuzhou University Fuzhou China

there exist many problems in intrusion detection system such as large number of data volume and features, data redundancy and so on, which seriously affected the efficiency of the assessment. In this paper, we propose an approach called EFSA-CP to intrusion detection based on Cloud model and improved multi-objective particle swarm optimization. The algorithm evaluates the characteristics of the attribute weights by the Cloud model and generates the optimal feature subsets which achieve the best trade-off between detection rate and rate of false alarm by MOPSO, which solves the problem of feature redundancy and helps improve the speed of the evaluation. Experimental results show that EFSA-CP can solve the feature selection problem of intrusion detection effectively. It can also achieve balanced detection performance on different types of attacks, with better convergence at the same time.

关键词： component Cloud model feature selection intrusion detection multi-objective particle swarm optimization

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Applying multi-objective particle swarm optimization to Maintenance Scheduling for CNC Machine Tools

Applying Multi-objective Particle Swarm Optimization to Main...

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2014 International Conference on Vehicle,Mechanical and Electrical Engineering(ICVMEE 2014)

作者： Zhihui Yao Min Zhou School of Wuhan University of Science and Technology

This paper focuses on the maintenance scheduling for CNC machine tools.A bi-objective mathematical model is built with the repair time and maintenance cost.A multi-objective particle swarm optimization(MOPSO),which combines the global best position adaptive selection and local search,is proposed to solve the mathematical *** results show that MOPSO has a better performance than other method for solving the maintenance *** also show that MOPSO is an effective algorithm that has strong convergence.

关键词： CNC machine tools maintenance scheduling multi-objective particle swarm optimization

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A modified multi-objective sorting particle swarm optimization and its application to the design of the nose shape of a high-speed train

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ENGINEERING APPLICATIONS OF COMPUTATIONAL FLUID MECHANICS 2015年第1期9卷 513-527页

作者： Yao, Shuanbao Guo, Dilong Sun, Zhenxu Yang, Guowei CSR Qingdao Sifang Locomot Co Ltd Natl Engn Res Ctr High Speed EMU Qingdao Peoples R China Chinese Acad Sci Inst Mech Key Lab Mech Fluid Solid Coupling Syst Beijing 100080 Peoples R China

Based on the concepts of niche count and crowding distance, a modified multi-objective particle swarm optimization (MPSO) is introduced. The niche count and crowding distance are used to determine the globally best particle across four test cases using an external file. A comparative analysis was carried out between MPSO and non-dominated sorting multi-objective adaptive genetic algorithms, both real-coded and binary-coded. The results show that MPSO based on the crowding distance is best for getting the Pareto front, especially for problems with high-dimensional and non-continuous Pareto fronts. In order to verify the efficiency of MPSO in solving engineering problems, the optimal design of the aerodynamic nose shape of high-speed trains was undertaken using a modified vehicle modeling function (MVMF) parametric method. Taking the aerodynamic drag of the whole train (Cd) and the aerodynamic lift of the trailer car (Cl) as the optimization goals, the Kriging surrogate model was introduced to reduce the computational time, and the MPSO based on crowding distance was used to find the Pareto front. The optimization results show that MPSO is efficient at getting the Pareto front;compared to the original shape, the Cd and Cl of the optimal shape are reduced by 1.6% and 29.74%, respectively.

关键词： multi-objective particle swarm optimization crowding distance VMF parameterization aerodynamic shape high-speed trains

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A Feature Transformation Method Based on multi objective particle swarm optimization for Reducing Support Vector Machine Error 2

A Feature Transformation Method Based on Multi Objective Par...

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2nd International Conference on Pattern Recognition and Image Analysis IPRIA

作者： Hoseinkhani, Fatemeh Nasersharif, Babak Qazvin Univ Med Sci Dept Educ Qazvin Iran KN Toosi Univ Technol Dept Elect & Comp Engn Tehran Iran

ISBN: (纸本)9781479984459

Discriminative methods are used for increasing pattern recognition and classification accuracy. These methods can be used as discriminative transformations applied to features or they can be used as discriminative learning algorithms for the classifiers. Most of discriminative feature transformation measures don't consider the classification method errors and information. In this paper, we propose a feature transformation method for support vector machine to consider both features discrimination and classification error. To this end, we use multi-objective particle swarm optimization (multi-PSO), where we consider two mentioned criteria as objectives in multi-PSO fitness function. Experimental results on UCI dataset show that the proposed multi-PSO based feature transformation method outperform other conventional methods of feature transformation when it is used as a preprocessing step for SVM.

关键词： Feature Transformation Discrimination multi-objective particle swarm optimization Support Vector Machine

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Handling Smart-Grid with a multi-objective particle swarm optimization algorithm

Handling Smart-Grid with a Multi-objective Particle Swarm Op...

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International Renewable and Sustainable Energy Conference

作者： Mourad SELLAM Mohamed AKHERRAZ Yassine SAYOUTI Team of Power Systems and Command (EEPC) Mohammadia School of Engineering (EMI) Rabat Morocco

ISBN: (纸本)9781467378956

In this paper, an improved multi-objective particle swarm optimization (MOPSO) of a Smart-grid energy flow in order to achieve the lower cost of the consumed energy is presented. The Smart-Grid is provided with different energy sources: fossil energy sources, renewable energy sources (photovoltaic panels, wind turbines ...) and consumers' network. The used algorithm is based on the approach of multi-objective particle swarm optimization algorithms. It is adapted to a generic Smart-Grid model in order to optimize the total cost of the consumers supporting constraints of Smart-Grids and integrating renewable energy sources. The algorithm is simulated on Matlab giving an optimal solution (called Pareto optimal) corresponding to an optimal cost. At the end of this paper, we will analyze the results of the simulation of this algorithm.

关键词： multi-objective particle swarm optimization Smart Grid Fossil energy sources Photovoltaic panels Wind turbines

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Handling multi-objective optimization problems with a comprehensive indicator and layered particle swarm optimizer

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2023年第8期20卷 14866-14898页

作者： Zhang, Xianzi Liu, Yanmin Yang, Jie Liu, Jun Shu, Xiaoli Guizhou Minzu Univ Sch Data Sci & Informat Engn Guiyang 550025 Peoples R China Zunyi Normal Coll Sch Math Zunyi 563002 Peoples R China

The multi-objective particle swarm optimization algorithm has several drawbacks, such as premature convergence, inadequate convergence, and inadequate diversity. This is particularly true for complex, high-dimensional, multi-objective problems, where it is easy to fall into a local optimum. To address these issues, this paper proposes a novel algorithm called IMOPSOCE. The innovations for the proposed algorithm mainly contain three crucial factors: 1) an external archive maintenance strategy based on the inflection point distance and distribution coefficient is designed, and the comprehensive indicator (CM) is used to remove the non-dominated solutions with poor comprehensive performance to improve the convergence of the algorithm and diversity of the swarm;2) using the random inertia weight strategy to efficiently control the movement of particles, balance the exploration and exploitation capabilities of the swarm, and avoid excessive local and global searches;and 3) offering different flight modes for particles at different levels after each update to further enhance the optimization capacity. Finally, the algorithm is tested on 22 typical test functions and compared with 10 other algorithms, demonstrating its competitiveness and outperformance on the majority of test functions.

关键词： distribution coefficient distance of inflection point multi-objective optimization multi-objective particle swarm optimization

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