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5th International Symposium on Intelligence Computation and Applications

作者： Shao, Yuxiang Chen, Qing Li, Cuihong China Univ Geosci Sch Comp Sci Wuhan 430074 Hubei Peoples R China Wuhan Inst Tech Sch Comp Sci & Engn Wuhan 430073 Peoples R China

ISBN: (纸本)9783642163876

In order to overcome the inherent deficiency in particle swarm optimization algorithm such as premature convergence, this paper presents crossover operator and mutation operator to improve particle swarm optimization algorithm, which is called IPSO. At the meantime, a new hybrid algorithm model is presented, which combine improved PSO algorithm and simulated annealing (SA) algorithm. The experimental results show that the proposed algorithm can reach the goal completely and the speed of convergence was greatly fast for optimization of Sphere, Griewank and Rastrigrin functions. The stability and robustness of proposed algorithm have been enhanced greatly. Its performance is superior to the standard PSO obviously.

关键词： particle swarm optimization algorithm Crossover operator Mutation operator Simulated annealing algorithm Optimize

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A Hybrid PSO/GA algorithm for Job Shop Scheduling Problem 1

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1st International Conference on swarm Intelligence

作者： Tang, Jianchao Zhang, Guoji Lin, Binbin Zhang, Bixi South China Univ Technol Sch Comp Sci & Engn Guangzhou 510641 Guangdong Peoples R China South Chain Normal Univ Comp Sch Guangzhou 510631 Peoples R China South China Univ Technol Coll Sci Guangzhou 510641 Guangdong Peoples R China Guangdong Univ Technol Sch Econ Management Guangzhou 510090 Peoples R China

ISBN: (数字)9783642134951

ISBN: (纸本)9783642134944

The job shop scheduling problem is a well-known NI) hard problem, on which genetic algorithm is widely used However. due to the lack of the major evolution direction. the effectiveness of the regular genetic algorithm is restricted In this paper. we propose a new hybrid genetic algorithm to solve the job shop scheduling problem The particle swarm optimization algorithm is introduced to get the initial population, and evolutionary genetic operations are proposed We validate the new method on seven benchmark datasets. and the comparisons with some existing methods verify as effectiveness

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

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A New optimization Identification Method for Fault Diagnosis

A New Optimization Identification Method for Fault Diagnosis

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2nd International Asia Conference on Informatics in Control, Automation and Robotics (CAR)

作者： Tian, WenJie Liu, JiCheng Beijing Union Univ Automat Inst Beijing Peoples R China

ISBN: (纸本)9781424451944

Fault diagnosis of electronic circuit is important for safety of the device and relevant power system. In the study, support vector regression (SVR) classifiers combined with the particle swarm optimization algorithm (POSA) are applied to construct diagnostic model of electronic circuit, and the diagnostic system structure of electronic circuit is presented on the basis of the model. It is powerful for the practical problem with small sampling, nonlinear and high dimension, which is very suitable for online fault diagnosis. Utilizing the character that principal components analysis algorithm can keep the discernability of original dataset after reduction, reduce of the original dataset is calculated and used to train individual SVR for ensemble, and consequently, increase the detection accuracy. The test results show that the proposed method is a promised method owning to its high diversity, high detection accuracy and faster speed in fault diagnosis. The experimental result shows that this fault detection method is feasible and effective.

关键词： Terms support vector regression fault Diagnosis principal components analysis reduction particle swarm optimization algorithm

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Research on Location model of Risk Pooling with Compensation Policy

Research on Location model of Risk Pooling with Compensation...

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International Conference on Logistics Systems and Intelligent Management

作者： Wang, Fei Jia, Tao Hu, Xinbu Changan Univ Sch Econ & Management Xian 710064 Peoples R China Xian Int Studies Univ Inst Human Geog Xian Peoples R China Xi An Jiao Tong Univ Sch Management Xian Peoples R China Engn Coll Armied Police Force Architecture Engn Dept Xian 710086 Peoples R China

ISBN: (纸本)9781424473281

We summarized recent research aimed at expanding the context of facility location decisions to incorporate additional features of a supply chain including variable construction cost, inventory management, transportation cost, etc.. Authors expended location model of risk pooling with variable construstion cost (LMRPVCC) to construct location model of risking pool with variable construction cost with compensation policy. In a buyer market, logistics corporations may apply compensation policy in order to hold more customers by giving a discount transportation charge. Authors build a square nonlinear 0-1 integer-programming model and use particle swarm optimization algorithm to find suboptimum solutions. The numerical examples are given separately along with the models to evaluate the effectiveness of the model. According to the computational results, we can draw these conclusions: transport costs factor beta is positively related with the objective function value. Compensation cost factor W is positively related with the objective function value. Distribution radius D-r is negatively related with the objective function value.

关键词： Location particle swarm optimization algorithm Distribution Center Compensation Policy

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Using Heuristic Rules to Enhance a Multiswarm PSO for Dynamic Environments

Using Heuristic Rules to Enhance a Multiswarm PSO for Dynami...

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2010 IEEE World Congress on Computational Intelligence

作者： del Amo, Ignacio G. Pelta, David A. Gonzalez, Juan R. Univ Granada Models Decis & Optimizat MODO Res Grp Dept Comp Sci & Artificial Intelligence E-18071 Granada Spain

ISBN: (纸本)9781424481262

The particle swarm optimization (PSO) algorithm has been successfully applied to dynamic optimization problems with very competitive results. One of its best performing variants, the mQSO is based on an atomic model, with quantum and trajectory particles. This work introduces a new version of this algorithm which uses heuristic rules for improving its performance. Two new rules are presented: one specifically designed for the mQSO, which locally bursts diversity after a change in the environment, and a second, more general one, which globally increases diversity in a precise way, without disturbing the intensification of the search. The new version with rules is tested against the original one using several variations of the Moving Peaks Benchmark and the Ackley function. The results show a drastic improvement in the performance of the algorithm.

关键词： Ackley function PSO algorithm dynamic environments dynamic optimization problems heuristic rules locally bursts diversity mQSO moving peaks benchmark multiswarm PSO particle swarm optimisation particle swarm optimization algorithm quantum computing quantum particles trajectory particles

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Soft Sensor Modeling Based on PSO-FNN for Lysine Fermentation Process

Soft Sensor Modeling Based on PSO-FNN for Lysine Fermentatio...

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2nd International Asia Conference on Informatics in Control, Automation and Robotics (CAR)

作者： Huang, Yonghong Xia, Chenglin Sun, Yukun Zhu, Xianglin Wang, Yuejun Jiangsu Univ Sch Elect & Informat Engn Zhenjiang Peoples R China Chinese Acad Fishery Sci Yellow Sea Fisheries Res Inst Qingdao Peoples R China

ISBN: (纸本)9781424451944

In fermentation process, fuzzy neural networks (FNN) is a novel machine learning method of soft sensor modeling, while the typical algorithm of FNN is inefficient because they can not optimize fuzzy rules and has long training time. Biological parameters can be measured online in real time which is helpful for the control of process optimization. So this paper introduces the use of the particle swarm optimization (PSO) for training FNN. Unlike the conventional back-propagation technique, the adaptation of the weights of the FNN approximator is done on-line using PSO. The PSO is based on the least squares error minimization with random initial condition and without any off-line pre-training. Experiment results show that, in contrast to the traditional fuzzy neural networks, the method has good prediction and is suitable to practical applications.

关键词： particle swarm optimization algorithm fuzzy neural networks soft sensor modeling

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Structural Design of Optimized Polynomial Radial Basis Function Neural Networks

Structural Design of Optimized Polynomial Radial Basis Funct...

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7th International Symposium on Neural Networks

作者： Kim, Young-Hoon Kim, Hyun-Ki Oh, Sung-Kwun Univ Suwon Dept Elect Engn Hwaseong Si 445743 Gyeonggi Do South Korea

ISBN: (纸本)9783642132773

In this paper, we introduce optimization methods of Polynomial Radial Basis Function Neural Network (pRBFNN). The connection weight of proposed pRBFNN is represented as four kinds of polynomials, unlike in most conventional RBFNN constructed with constant as connection weight. Input space in partitioned with the aid of kernel functions and each kernel function is used Gaussian type. Least Square Estimation (LSE) is used to estimate the coefficients of polynomial. Also, in order to design the optimized pRBFNN model, center value of each kernel function is determined based on C-Means clustering algorithm, the width of the RBF, the polynomial type in the each node, input variables are identified through particle swarm optimization (PSO) algorithm. The performances of the NOx emission process of gas turbine power plant data and Automobile Miles per Gallon (MPG) data was applied to evaluate proposed model. We analyzed approximation and generalization of model.

关键词： Polynomial Radial Basis Function Neural Networks (pRBFNN) C-Means particle swarm optimization algorithm Machine Learning data

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The Coordinate Control of Ship Steering and Main Propulsion in Constrict Waters for Collision Avoidance

The Coordinate Control of Ship Steering and Main Propulsion ...

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International Conference on Logistics Systems and Intelligent Management

作者： Zhuo Yongqiang Guo Chen Dalian Fisheries Univ Coll Ocean Engn Dalian 116023 Peoples R China

ISBN: (纸本)9781424473281

In order to facilitate a precise collision avoidance scheme capability for ships under low speed in constrict water, a novel intelligent coordinate control approach of ship steering and main propulsion is proposed. In the research an innovative self-training optimizing search method is presented. The optimizing process is based on particle swarm optimization algorithm and off-line training data. The training data is obtained from trial manoeuvres based on computer simulation. This support system has been developed to help ship operators make a precise collision avoidance decision, whilst simultaneously reducing the burden of bridge data processing. The application results show that the designed intelligent coordinate controller performed well to implement the optimizing control of ship collision avoidance.

关键词： Coordinate Control Intelligent Collision Avoidance Trial Manoeuvre particle swarm optimization algorithm

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Solving uncapacitated multilevel lot-sizing problems using a particle swarm optimization with flexible inertial weight

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COMPUTERS & MATHEMATICS WITH APPLICATIONS 2009年第11-12期57卷 1748-1755页

作者： Han, Yi Tang, Jiafu Kaku, Iko Mu, Lifeng Northeastern Univ Key Lab Integrated Automat Proc Ind Minist Educ Shenyang 110004 Peoples R China Akita Prefectural Univ Dept Management Sci & Engn Honjo 0150015 Japan

The multilevel lot-sizing (MLLS) problem is a key production planning problem in materials requirements planning (MRP) system. The MLLS problem deals with determining the production lot-sizes of various items appearing in the product structure over a given finite planning horizon to minimize the production cost, the inventory carrying cost, the back ordering cost and etc. This paper proposed a particle swarm optimization (PSO) algorithm for solving the uncapacitated MLLS problem with assembly structure. All the mathematical operators in our algorithm are redefined and the inertial weight parameter can be either a negative real number ora positive one. The feasibility and effectiveness of our algorithm are investigated by comparing the experimental results with those of a genetic algorithm (GA). (C) 2008 Elsevier Ltd. All rights reserved.

关键词： particle swarm optimization algorithm Multilevel lot-sizing problem Assembly structure Uncapacitated Genetic algorithm

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The optimization of Partner Selection Based on Multi-Agent

The Optimization of Partner Selection Based on Multi-Agent

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22nd Chinese Control and Decision Conference

作者： Zhou Wei Bu Yan-ping East China Univ Sci & Technol Sch Business Shanghai 200237 Peoples R China Shanghai Jiao Tong Univ Sch Technol Shanghai 201101 Peoples R China

ISBN: (纸本)9781424451821

The application of multi-agent technology to partner selection problem of virtual enterprise in Internet is studied. The partner selection model is constructed based on multi-agent technology. In this construction, each management function, resource and sub-task is defined as an agent. The communication, cooperation and negotiation among agents are analyzed. Immune particle swarm optimization (PSO) algorithm is proposed and applied to the optimization of partner selection and task scheduling agent. The general requirements of partner selection model of virtual enterprise based on multi-agent structure are satisfied.

关键词： particle swarm optimization algorithm virtual enterprise agent partner selection

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