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检索条件"主题词=Particle swarm optimization algorithm"

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Dimensional Synthesis of a Four-bar Linkage Mechanism via a PSO-based Cooperative Neural Network Approach 25

Dimensional Synthesis of a Four-bar Linkage Mechanism via a ...

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25th Iranian Conference on Electrical Engineering (ICEE)

作者： Asaeikheybari, Golnoush Lafmejanit, Amir Salimi Kalhorj, Ahmad Masouleh, Mehdi Tale Shahid Beheshti Univ Dept Elect Engn Tehran Iran Univ Tehran Fac New Sci & Technol Human & Robot Interact Lab Tehran Iran Univ Tehran Sch Elect & Comp Engn Control & Intelligent Proc Ctr Excellence Tehran Iran Univ Tehran Human & Robot Interact Lab Sch Elect & Comp Engn Tehran Iran

ISBN: (纸本)9781509059638

The dimensional synthesis problem is one of the challenging problems in robotics which has initiated several mathematical challenges. In this paper, a novel algorithm is proposed based on combination of particle swarm optimization (PSO) and Cooperative Neural Network (CNN) for solving synthesis problem of a four-bar linkage which leads to an optimization problem. The cooperative network, so-called PS-CNN, consists of memory-retaining particles which collaborate together based on PSO algorithm in a cooperative interaction converge to the optimal dimensional synthesis solution. In the complete-connected network, each neuron provides a solution. Thereby, solutions are updated according to the neurons' memory, their interaction with other neurons and the global best solution of the neurons in order to provide a proper solution to the optimization problem. The objective of the optimization problem is to minimize the distance of the robot's end-effector from the 5 prescribed points by the user when traversing them. Simulation results reveal the desirable performance of the PS-CNN for robot synthesis with higher complexities. Furthermore, the proposed approach opens an avenue to extend it.

关键词： Interactive Neural Network Four-bar Linkage particle swarm optimization algorithm Robot Synthesis

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Resource Allocation Problem in Port Project Portfolio Management 7

Resource Allocation Problem in Port Project Portfolio Manage...

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7th International Joint Conference on Computational Sciences and optimization (CSO)

作者： Ye, Kaibin Shi, Xiaolun Li, Huijian Shi, Ning South China Agr Univ Coll Sci Guangzhou Guangdong Peoples R China Sun Yat Sen Univ Sch Business Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781479953721

Due to the increased complexity of port operations, port project portfolio management has become more and more essential. However, in port project portfolio management, the demand for resources is usually uncertain. In order to reduce the cost and the difficulty of resource allocation, we pay more attention to balancing resource allocation than minimizing the maximum value of the consumption of resources. Thus we consider a resource leveling problem in port project portfolio management. In this paper, we introduce a combination of entropy weight and particle swarm optimization algorithm. This proposed method is applied to a case study. And the results show that this method can achieve satisfactory performance in practice.

关键词： port project portfolio management resource leveling particle swarm optimization algorithm entropy weight

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Modelisation and optimization of clutch magnet actuator topologies

Modelisation and Optimization of clutch magnet actuator topo...

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42nd Annual Meeting of the IEEE-Industry-Applications-Society

作者： Pauvert, V. Bernard, N. Zaim, M. E. Bonnefous, J. IREENA Bd UnivBP406 F-44602 Saint Nazaire France Defontaine SA La Bruffiere France

ISBN: (纸本)9781424412600

This paper presents the study of clutch magnet actuators. Two topologies, considering different magnetic constraints of saturation are designed in order to generate powerful engaging force in a limited volume. The magnetic models are based on a reluctance equivalent magnetic circuit considering the magnet flux leakages. The model results are then compared and validated with finite element computations and measurements on different built actuators. Finally, the validated analytical models are coupled to a particle swarm optimization algorithm in order to maximize the transmitted torque or the torque to volume ratio. The study shows that a reluctance circuit model validated by some experimentation and coupled to a particle swarm algorithm gives a solid and efficient basis for the design and the optimization of clutch magnet actuators.

关键词： Lining clutch analytical modeling magnet actuator leakage flux reluctance equivalent circuit particle swarm optimization algorithm

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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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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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6th International Conference on Soft Computing and Intelligent Systems (SCIS) / 13th International Symposium on Advanced Intelligence Systems (ISIS)

作者： Kashan, Ali Husseinzadeh Karimiyan, Somayyeh Karimiyan, Morteza Kashan, Mina H. Islamic Azad Univ Dept Syst & Ind Engn Sci & Res Branch Tehran Iran Int Inst Earthquake Engn & Seismol Dept Earthquake Engn Tehran Iran Islamic Azad Univ Dept Earthquake Engn Sci & Res Branch Tehran Iran Tarbiat Modares Univ Fac Engn Dept Ind Engn Tehran Iran

ISBN: (纸本)9781467327435;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 postmatch 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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Lifetime Enhancement of Wireless Sensor Networks by using Sine Cosine optimization algorithm 7

Lifetime Enhancement of Wireless Sensor Networks by using Si...

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7th International Conference on Reliability, Infocom Technologies and optimization - Trends and Future Directions (ICRITO)

作者： Pandey, Ashish Rajan, Abhishek Nandi, Arnab Natl Inst Technol Silchar Dept Elect & Commun Engn Cachar 788010 Assam India Natl Inst Technol Sikkim Dept Elect & Elect Engn Sikkim 737139 India

ISBN: (纸本)9781538646922

Wireless sensor networks (WSNs) have numerous applications from the measurements of atmospheric quantity, tracking to the various medical applications. Sensor nodes in the networks are mostly battery powered and they are having limited lifetime as it is not always feasible to replace their battery. To overcome this constraint of energy various energy management scheme have been proposed and implemented. In this paper we have used Sine Cosine algorithm (SCA) in routing and clustering for enhancing the working lifetime of WSNs. As the cluster head (CH) in the networks having more energy expenditure so we have also used some higher energy nodes to perform CHs operations for the enhancement of the lifetime. We have compared the results with the other algorithms namely particle swarm optimization (PSO) algorithm, genetic algorithm (GA) and least distance clustering (LDC) algorithm. Results are also have been studied after moving the base station at different locations.

关键词： WSNs Sine Cosine optimization algorithm Genetic algorithm particle swarm optimization algorithm PSO Clustering Routing

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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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Computing Single Step Operators of Logic Programming in Radial Basis Function Neural Networks

Computing Single Step Operators of Logic Programming in Radi...

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21st National Symposium on Mathematical Sciences (SKSM)

作者： Hamadneh, Nawaf Sathasivam, Saratha Choon, Ong Hong Univ Sains Malaysia Sch Math Sci Usm 11800 Penang Malaysia

ISBN: (纸本)9780735412415

Logic programming is the process that leads from an original formulation of a computing problem to executable programs. A normal logic program consists of a finite set of clauses. A valuation I of logic programming is a mapping from ground atoms to false or true. The single step operator of any logic programming is defined as a function (T-p : I -> I). Logic programming is well-suited to building the artificial intelligence systems. In this study, we established a new technique to compute the single step operators of logic programming in the radial basis function neural networks. To do that, we proposed a new technique to generate the training data sets of single step operators. The training data sets are used to build the neural networks. We used the recurrent radial basis function neural networks to get to the steady state (the fixed point of the operators). To improve the performance of the neural networks, we used the particle swarm optimization algorithm to train the networks.

关键词： Single step operator particle swarm optimization algorithm recurrent radial basis function neural networks

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Binary Cat swarm optimization for Cryptanalysis 11

Binary Cat Swarm Optimization for Cryptanalysis

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IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS)

作者： Amic, Seeven Soyjaudah, K. M. Sunjiv Ramsawock, Gianeshwar Univ Mascareignes Dept Software Engn Pamplemousses Mauritius Univ Mauritius Dept Elect & Elect Engn Reduit Mauritius

ISBN: (纸本)9781538623473

This research paper investigates the relevance of the Cat swarm optimization (CSO) algorithm to cryptanalysis. It is a relatively new evolutionary metaheuristic technique to solve those problems which belong to NP-hard class based on the swarm intelligence of felids, commonly known as cats. Cryptanalysis is an active field of experimental research as it can consolidate or depreciate the viability of modern ciphers. To the extent of our knowledge, there is no evidence that the CSO has ever been applied to the cryptanalysis problem. Experimental outcomes demonstrate that the Binary Cat swarm optimization (BCSO) algorithm is efficient for the cryptanalysis of the Data Encryption Standard (DES) using chosen plaintext attack. Further, it produces optimal keys with higher fitness as compared to particle swarm optimization. This paper shows that BCSO can be successfully utilized for the cryptanalysis of block ciphers with promising outcomes.

关键词： Cryptanalysis DES swarm intelligence Binary Cat swarm optimization algorithm particle swarm optimization algorithm Fitness Function

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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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