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第28届中国控制与决策会议

作者： Cao Li Xiaoyu Liu School of Information Science and Engineering Wuhan University of Science and Technology

ISBN: (纸本)9781467397155

This paper presents a way of combining BP(Back Propagation) neural network and an improved PSO(particle swarm optimization) algorithm to predict the earthquake *** is known that the BP neural network and the normal PSO-BP neural network have some defeats,such as the slow convergence rate,easily falling into local minimum *** improving the properties of PSO,some proposed the linear decreasing inertia weight ***,this paper uses a nonlinear decreasing inertia weight in PSO to get a faster training speed and better optimal *** with the linear decreasing strategy,the inertia weight in our nonlinear method has a faster declining speed in the early iteration,which can enhance the searching *** the late iteration,the inertia weight has a slower declining speed to avoid trapping in local minimum *** we apply the improved PSO to optimize the parameters of BP neural *** the end,the improved PSO-BP neural network is applied to earthquake *** simulation results show that the proposed improved PSO-BP neural network has faster convergence rate and better predictive effect than the BP neural network and the normal PSO-BP neural network.

关键词： BP neural network particle swarm optimization algorithm Inertia Weight Earthquake Prediction

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A Modified League Championship algorithm for Numerical Function optimization Via Artificial Modeling of the "Between Two Halves Analysis"

A Modified League Championship Algorithm for Numerical Funct...

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International Conference on Soft Computing and Intelligent Systems

作者： Ali Husseinzadeh Kashan Somayyeh Karimiyan Morteza Karimiyan Mina H. Kashan Department of Systems and Industrial Engineering Science and Research Branch Islamic Azad University Department of Earthquake Engineering International Institute of Earthquake Engineering and Seismology Department of Earthquake Engineering Science and Research Branch Islamic Azad University Department of Industrial Engineering Faculty of Engineering Tarbiat Modares University

ISBN: (纸本)9781467327428

Inspired by the competition of sport teams in a sport league, the League Championship algorithm (LCA) has been introduced recently for optimizing nonlinear continuous functions. LCA tries to metaphorically model a league championship environment wherein a number of individuals, as artificial sport teams, play in pairs in an artificial league for several weeks (iterations) based on a league schedule. Given the playing strength (fitness value) along with a team intended formation (solution) in each week, the game outcome is determined in terms of win or loss and this will serve as a basis to direct the search toward fruitful areas. At the heart of LCA is the artificial post-match analysis where, to generate a new solution, the algorithm imitates form the strengths/weaknesses/opportunities/threats (SWOT) based analysis followed typically by coaches to develop a new team formation for their next week contest. In this paper we try to modify the basic algorithm via modeling a between two halves like analysis beside the post-match SWOT analysis to generate new solutions. Performance of the modified algorithm is tested with that of basic version and the particle swarm optimization algorithm (PSO) on finding the global minimum of a number of benchmark functions. Results testify that the improved algorithm called RLCA, performs well in terms of both final solution quality and convergence speed.

关键词： Numerical function optimization League championship algorithm particle swarm optimization algorithm

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Multi-performance optimization of Cement Blending Process

Multi-performance Optimization of Cement Blending Process

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第三十届中国控制会议

作者： LI Junli,MAO Jianlin,ZHANG Guanghui College of Information Engineering and Automation,Kunming University of Science and Technology,Kunming 650051,P.R.China

In order to effectively solve the blending optimization problem in cement production process,an optimization model integrating production indices,the cost and its solution method are ***,the cost and the rate values of raw materials are incorporated into the optimization model respectively as the objective *** function is used to transform optimization problem with constraints into an unconstrained *** then particle swarm optimization algorithm(PSO) is applied to search the optimum *** the optimization information of swarm becomes stagnant,the "inertia weight" operator,cross and mutation operations based on protection strategy are introduced in the optimization process that make the algorithm to maintain diversity and better convergence *** calculation result shows that the presented method can effectively improve the global search ability and convergence *** optimization result can improve the passing rate of indicators by reducing production cost and the harmful ingredient of the mixed material.

关键词： Blending optimization Penalty function particle swarm optimization algorithm Inertia weight Protection strategy

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Feature Subset Selection -based Fault Diagnoses for Automobile Engine

Feature Subset Selection -based Fault Diagnoses for Automobi...

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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： Lifang Kong Shisong Zhu Zhe Wang Xuzhou Air Force College Jiangsu Xuzhou 221008 China Xuzhou Air Force College Jiangsu Xuzhou 221002 China

according to the stability of the membership degree and low identification rate in fuzzy inference system, this dissertation proposes the application of adaptive neural network-based fuzzy inference system to engine error diagnosis. To reduce the impact of excessive parameters on classification accuracy and cost, it also raises an asynchronous parallel particle swarm optimization method applied to the selection of feature subset. The method use uniform mutation operator, balancing effectively the particles' ability to search globally and to develop. The asynchronous parallel particle swarm optimization algorithm(AP-PSO) that is used to select the feature subset is a potential feature subset that carries the characteristic of firstly initializing every particle as the selection question. Then, the method adopts improved asynchronous parallel particle swarm algorithm to conduct optimal searching based on the particle swarm that include several feature subsets and evaluate the classification ability (adaptive value) of the feature subset selected by way of Support Vector Machine. Finally, the optimal feature subset is *** verification of the build diagnosis model with data of engine tests, it has been found that the recognition accuracy attain to 98.72％, training error falling to *** experiment indicates that the recognition rate of ANFIS system is significantly better than independent neural network reasoning system, fuzzy inference system.

关键词： adaptive neural fuzzy interference system fault diagnosis particle swarm optimization algorithm feature subset selection

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Hybrid Genetic algorithm for Flow Shop Scheduling Problem

Hybrid Genetic Algorithm for Flow Shop Scheduling Problem

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2010 International Conference on Intelligent Computation Technology and Automation(2010 智能计算技术与自动化国际会议 ICICTA 2010)

作者： Jianchao Tang Binbin Lin Guoji Zhang Bixi Zhang School of Computer Science and Engineering South China University of Technology Guangzhou China Com School of Science South China University of Technology Guangzhou China School of Economics and Management Guangdong University of Technology Guangzhou China

The flow shop scheduling problem (FSSP) is a NPHARD combinatorial problem with strong industrial background. Among the meta-heuristics, genetic algorithms attracted a lot of attention. However, lacking the major evolution direction, the effectiveness of regular genetic algorithm is restricted. In this paper, the particle swarm optimization algorithm (PSO) is introduced for better initial group. By combining PSO with GA, a hybrid optimization algorithm for FSSP is proposed. This method is validated on a series of benchmark datasets. Experimental results indicate that this method is efficient and competitive compared to some existing methods.

关键词： hybrid algorithm genetic algorithm particle swarm optimization algorithm flow shop scheduling problem

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Combining CA and PSO to Solve Flexible Job shop Scheduling Problem

Combining CA and PSO to Solve Flexible Job shop Scheduling P...

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第26届中国控制与决策会议

作者： ZHOU Wei BU Yan-ping ZHOU Ye-qing School of Business East China University of Science and Technology School of Technology Shanghai Jiaotong University School of Mathematical sciences Fudan University

The flexible job shop scheduling problem(FJSP) is an extension of the classic job shop scheduling problem(JSP),which breaks through the uniqueness of limit resources,allows a procedure in many machines processing and one machine processing many kinds of different types of *** is more practical and complex than *** computational complexity of FJSP is much higher,which disables exact solution methods and makes heuristic approaches more qualified.A hybrid optimization algorithm,CPSO,based on the cultural algorithm and particle swarm optimization algorithm,is proposed in this paper to solve the *** objective is to minimize *** results show that this hybrid method is able to solve efficiently these kinds of problems.

关键词： cultural algorithm particle swarm optimization algorithm flexible job shop scheduling problem makespan

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Optimal design of a BLDC motor using African vulture optimization algorithm

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e-Prime - Advances in Electrical Engineering, Electronics and Energy 2024年 7卷

作者： Tripathy, Surya Narayan Kundu, Shubhasri Pradhan, Arjyadhara Samal, Padarbinda School of Electrical Engineering KIIT Deemed to be University India

BLDC motors are extensively favoured in robots, electric vehicles, and many industrial applications due to their superior torque, efficiency, and speed control features as compared to traditional motors. In this paper, collective intelligence-based optimization approaches are used to improve the cost-effectiveness, volume, and total loss of a BLDC motor. The geometric dimensions of the motor are considered the optimization parameters. The study employs several optimization algorithms, such as the Whale optimization algorithm (WOA), Grey Wolf optimization (GWO), Cuckoo Search algorithm (COA), Genetic algorithm (GA), particle swarm optimization (PSO), and the African Vulture optimization algorithm (AVOA), used for the optimal BLDC motor design. The investigation can provide data on the convergence rates and optimal design parameters of a BLDC motor through optimization approaches. The standard deviation has also been calculated for each optimization technique to assess the stability and consistency of finding the optimum solution. The paper also includes a sensitivity analysis of the AVOA regulating parameter to obtain the best solutions. In comparison to GA, PSO, COA, GWO, and WOA, the AVOA technique achieves faster convergence. Additionally, the ANOVA test is employed as a statistical test to validate the efficacy of the AVOA. According to the results, AVOA outperforms all other optimization algorithms for the design of the BLDC motor. © 2024 The Author(s)

关键词： Brush-less DC motors Cuckoo search algorithm particle swarm optimization algorithm Whale optimization algorithm

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Prediction of concrete material based on PSO algorithm

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Jianzhu Jiegou Xuebao/Journal of Building Structures 2006年第SUPPL.期27卷 722-725页

作者： Liang, Benliang Sun, Fuxue State Key Laboratory for Disaster Reduction in Civil Engineering Tongji University Shanghai 200092 China Department of Geotechnical Engineering Tongji University Shanghai 200092 China

Concrete carbonation was affected by several factors. On the basis of this, the BP networks input vectors were constructed. Through the introduction of PSO algorithm, the connection weights of the model can be optimized, overcoming the shortcomings of the inefficiency in terms of convergence and the great possibility being stuck in a local minimum. The computation indicated that the accuracy and convergence velocity processed by this method is much better than that only adopted by BP algorithm.

关键词： Concrete carbonation Durability Neural networks particle swarm optimization algorithm

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Bio Inspired Approach for Load Balancing to Reduce Energy Consumption in Cloud Data Center

Bio Inspired Approach for Load Balancing to Reduce Energy Co...

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International Conference on Communication, Control and Intelligent Systems

作者： Akhil Goyal Navdeep S. Chahal Software Technology Division C-DAC

ISBN: (纸本)9781467375429

Cloud computing has made it feasible to access various IT resources through a high speed network from anywhere in the world. Constant increasing demand of cloud computing is equally popular in consumers as well as providers. But along with advancement every technology is also associated with some ill effects. On same path, cloud computing also accompanies a serious issue with it and that issue is energy consumption. In this paper Firefly algorithm has been selected as a proposed bio-inspired approach to perform load balancing to reduce energy consumption in cloud data center. Further, the results are compared with particle swarm optimization algorithm (PSO). The energy consumed in case of Firefly algorithm is less than energy consumed in PSO algorithm.

关键词： Cloud Computing Firefly algorithm particle swarm optimization algorithm Energy Consumption Load Balancing

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Optimal Relay Placement in Microgrids Considering Critical Clearing Time

Optimal Relay Placement in Microgrids Considering Critical C...

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IEEE International Power Engineering and optimization Conference

作者： S. J. Mirazimi B. Salehi M. Tadayon H. R. Karshenas Faculty of Engineering and Surveying University of Southern Queensland (USQ) Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan Distribution Network Department Behrad Consulting Engineers Isfahan

ISBN: (纸本)9781467350723

This paper is concerned with the optimal placement of protection devices in a microgrid using particle swarm optimization algorithm. One of the main advantages of Distributed Generation (DG) scheme and microgrids in modern distribution systems is the reduction of number of outages and the associated damages caused by them. This task is accomplished by supplying a feeder from multiple sources. In order to prevent generator instability in DGs connected to utility, it is necessary to improve the protective schemes of traditional distribution systems and also to use proper relaying and setting for DGs. All of the downstream overcurrent (OC) relays of each DG are coordinated together and also should be coordinated with OC relay that is installed on the Point of Common Coupling (PCC) which is set at Critical Clearing Time (CCT) as a definite time, to have a desirable performance on each outage. In this paper, by the use of graph theory, various branches of a feeder are identified and the constraints for using particle swarm optimization algorithm to optimize the location of protective equipment are derived. In the proposed algorithm, the location, type and direction of relays are optimized simultaneously.

关键词： Microgrid particle swarm optimization algorithm Protection-Coordination Graph theory

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