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Design of K-means clustering-based polynomial radial basis function neural networks (pRBF NNs) realized with the aid of particle swarm optimization and differential evolution

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NEUROCOMPUTING 2012年第1期78卷 121-132页

作者： Oh, Sung-Kwun Kim, Wook-Dong Pedrycz, Witold Joo, Su-Chong Univ Suwon Dept Elect Engn Hwaseong Si 445743 Gyeonggi Do South Korea Univ Alberta Dept Elect & Comp Engn Edmonton AB T6G 2G6 Canada Polish Acad Sci Syst Res Inst PL-01447 Warsaw Poland Wonkwang Univ Dept Comp Engn Iksan 570749 Chon Buk South Korea

In this paper, we introduce an advanced architecture of K-means clustering-based polynomial Radial Basis Function Neural Networks (p-RBF NNs) designed with the aid of Particle Swarm Optimization (PSO) and differential evolution (DE) and develop a comprehensive design methodology supporting their construction. The architecture of the p-RBF NNs comes as a result of a synergistic usage of the evolutionary optimization-driven hybrid tools. The connections (weights) of the proposed p-RBF NNs being of a certain functional character and are realized by considering four types of polynomials. In order to design the optimized p-RBF NNs, a prototype (center value) of each receptive field is determined by running the K-means clustering algorithm and then a prototype and a spread of the corresponding receptive field are further optimized through running Particle Swarm Optimization (PSO) and differential evolution (DE). The Weighted Least Square Estimation (WLSE) is used to estimate the coefficients of the polynomials (which serve as functional connections of the network). The performance of the proposed model and the comparative analysis involving models designed with the aid of PSO and DE are presented in case of a nonlinear function and two Machine Learning (ML) datasets (C) 2011 Elsevier B.V. All rights reserved.

关键词： Polynomial radial basis function neural networks (p-RBF NNs) K-means clustering Particle swarm optimization differential evolution algorithm Weighted least square estimation (WLSE)

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Three meta-heuristics to solve the no-wait two-stage assembly flow-shop scheduling problem

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SCIENTIA IRANICA 2013年第6期20卷 2275-2283页

作者： Mozdgir, A. Ghomi, S. M. T. Fatemi Jolai, F. Navaei, J. Payame Noor Univ Tehran 193954697 Iran Amirkabir Univ Technol Dept Ind Engn Tehran 424 Iran Univ Tehran Dept Ind Engn Coll Engn Tehran Iran Univ Windsor Intelligent Mfg Syst IMS Ctr Windsor ON N9B 3P4 Canada

This paper addresses the No-Wait Two-Stage Assembly Flow-shop Scheduling Problem (NWTSAFSP) with the objective of makespan minimization. The problem is a generalization of previously proposed general problem in the Two-Stage Assembly Flow Shop Scheduling Problem (TSAFSP). The TSAFSP is NP-hard, thus the NWTSAFSP is NP-hard too, and three meta-heuristic algorithms, namely, Genetic algorithm (GA), differential evolution algorithm (DEA) and Population-based Variable Neighborhood Search (PVNS) are proposed in this article to solve this problem. Computational results reveal that PVNS outperforms other algorithms in terms of average error and average Coefficient of Variation (CV). Nevertheless, GA has the least run time among the proposed algorithms. (C) 2013 Sharif University of Technology. All rights reserved.

关键词： No-wait assembly flow-shop Genetic algorithm differential evolution algorithm Population-based variable neighborhood search

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Training of Artificial Neural Networks Using differential evolution algorithm

Training of Artificial Neural Networks Using Differential Ev...

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2008 Conference on Human System Interactions (HSI 2008), vol.1

作者： Adam Slowik Michal Bialko Department of Electronics and Computer Science Koszalin University of Technology Koszalin Poland

In the paper an application of differential evolution algorithm to training of artificial neural networks is presented. The adaptive selection of control parameters has been introduced in the algorithm;due to this property only one parameter is set at the start of proposed algorithm. The artificial neural networks to classification of parity-p problem have been trained using proposed algorithm. Results obtained using proposed algorithm have been compared to the results obtained using other evolutionary method, and gradient training methods such as: error back-propagation, and Levenberg-Marquardt method. It has been shown in this paper that application of differential evolution algorithm to artificial neural networks training can be an alternative to other training methods.

关键词： Artificial intelligence Artificial neural network differential evolution algorithm Training method

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Identification of structure and parameters of rheological constitutive model for rocks using differential evolution algorithm

Identification of structure and parameters of rheological co...

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The 9th National Conference on Rheology

作者：苏国韶张小飞陈光强符兴义 College of Civil and Architecture Engineering Guangxi University State Key Laboratory of Geomechanics and Geotechnical Engineering Institute of Rock and Soil MechamicsChinese Academy of Sciences

To determine structure and parameters of a rheological constitutive model for rocks,a new method based on differential evolution(DE)algorithm combined with FLAC(a numerical code for geotechnical engineering)was proposed for identification of the global optimum coupled of model structure and its *** first,stochastic coupled mode was initialized,the difference in displacement between the numerical value and in-situ measurements was regarded as fitness value to evaluate quality of the coupled *** the coupled-mode was updated continually using DE rule until the optimal parameters were ***,coupled-mode was identified adaptively during back analysis *** results of applications to Jinping tunnels in China show that the method is feasible and efficient for identifying the coupled-mode of constitutive structure and its *** method overcomes the limitation of the traditional method and improves significantly precision and speed of displacement back analysis process.

关键词： rheological constitutive model rocks differential evolution algorithm identification FLAC3D

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A Modified differential evolution algorithm and Its Application in the Training of BP Neural Network

A Modified Differential Evolution Algorithm and Its Applicat...

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2008 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, Pages 949-1423

作者： Yuelin Gao Junmin Liu School of Information & Computation Science University of North National University Yinchuan Ningxia Province China

ISBN: (纸本)9781424424948

The paper is given a new modified differential evolution (MDE) algorithm in which a novel mutation operator is introduced. The MDE algorithm can obtain a good balance between global search and local search and was applied in BP neural network training. The numerical results demonstrate that the new MDE algorithm has the abilities of good global search and faster convergence speed and higher convergence accuracy. It can overcome the disadvantages of the traditional BP algorithm and reduce the training time and improve the training accuracy.

关键词： differential evolution algorithm mutation operator BP neural network application

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Multi-objective Reactive Power Management using differential evolution

Multi-objective Reactive Power Management using Differential...

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IEEE 5th India International Conference on Power Electronics (IICPE)

作者： Singh, Himmat Srivastava, Laxmi Madhav Inst Sci & Technol Dept Elect Engn Gwalior India

ISBN: (纸本)9781467309349

Reactive Power Management (RPM) is one of the most significant tasks for proper operation and control of a power system. Reactive Power Management reduces power system losses by adjusting the reactive power control variables such as generator voltages, transformer tap-settings and other sources of reactive power such as capacitor banks or FACTS devices. RPM provides better system voltage control resulting in improved voltage profile, system security, power transfer capability and overall system operation. RPM is a complex combinatorial optimization problem involving nonlinear functions having multiple local minima and nonlinear and discontinuous constraints. In this paper, the RPM problem is formulated as nonlinear constrained multi-objective optimization problem with equality and inequality constraints for minimization of power losses and voltage deviation simultaneously. This multi-objective problem is solved using differential evolution (DE), which is a population based search algorithm. Weighing factor method has been employed for finding Pareto optimal set for RPM problem. Fuzzy membership function is used to find the best compromise solution out of the available Pareto-optimal solutions. The proposed approach has been demonstrated on the standard IEEE-30 bus system.

关键词： Reactive power management differential evolution algorithm power loss minimization voltage deviation Pareto-optimal solutions

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Maintenance Scheduling of Distribution System with Optimal Economy and Reliability

Maintenance Scheduling of Distribution System with Optimal E...

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2013 Power Engineering and Automation Conference (PEAM 2013)

作者： Siyuan Hong Haifeng Li Fengjiao Wang School of Electrical Power South China University of Technology

With the continuous expansion of power distribution grid, the number of distribution equipments has become larger and larger. In order to make sure that all the equipments can operate reliably, a large amount of maintenance tasks should be conducted. Therefore, maintenance scheduling of distribution network is an important content, which has significant influence on reliability and economy of distribution network operation. This paper proposes a new model for mainten-ance scheduling which considers load loss, grid active power loss and system risk as objective functions. On this basis, differential evolution algorithm is adopted to optimize equipment maintenance time and load transfer path. Finally, the general distribution network of 33 nodes is taken for example which shows the maintenance scheduling model's effec-tiveness and validity.

关键词： Maintenance Scheduling Multi-Objective differential evolution algorithm Condition Based Maintenance

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Metaheuristic algorithms for the Quadratic Assignment Problem

Metaheuristic Algorithms for the Quadratic Assignment Proble...

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IEEE Symposium on Computational Intelligence In Production and Logistics Systems

作者： M. Fatih Tasgetiren Quan-Ke Pan P. N. Suganthan Ikbal Ece Dizbay Industrial Engineering Department Selcuk Yasar Campus Yasar University SKL of Synthetical Automation for Process Industries (Northeastern University) School of Electrical and Electronic Engineering Nanyang Technological University

ISBN: (纸本)9781467359047

This paper presents two meta-heuristic algorithms to solve the quadratic assignment problem. The iterated greedy algorithm has two main components, which are destruction and construction procedures. The algorithm starts from an initial solution and then iterates through a main loop, where first a partial candidate solution is obtained by removing a number of solution components from a complete candidate solution. Then a complete solution is reconstructed by inserting the partial solution components in the destructed solution. These simple steps are iterated until some predetermined termination criterion is met. We also present our previous discrete differential evolution algorithm modified for the quadratic assignment problem. The quadratic assignment problem is a classical NP-hard problem and its applications in real life are still considered challenging. The proposed algorithms were evaluated on quadratic assignment problem instances arising from real life problems as well as on a number of benchmark instances from the QAPLIB. The computational results show that the proposed algorithms are superior to the migrating birds optimization algorithm which appeared very recently in the literature. Ultimately, 7 out of 8 printed circuit boards (PCB) instances are further improved.

关键词： Quadratic assignment problem Iterated greedy algorithm differential evolution algorithm Migrating birds optimization

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Application of algorithm used in Community Detection of Complex Network

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INTERNATIONAL JOURNAL OF FUTURE GENERATION COMMUNICATION AND NETWORKING 2013年第4期6卷 219-229页

作者： Wang, Guoshun Zhang, Xuan Jia, Guanbo Ren, Xiaoping Cent South Univ Changsha 410083 Hunan Peoples R China Natl Inst Metrol Beijing 100013 Peoples R China

A new algorithm named differential evolution algorithm for Community Detection (DEACD) was proposed in the paper. DEACD used DE as its search engine and used the network modularity as the fitness function to search for an optimal community partition of a network. In this algorithm, there is a modified binomial crossover mechanism to transmit some important information about the community structure in evolution effectively. In addition, a biased process and clean-up operation were employed in DEACD to improve the quality of the community partitions detected in evolution. Experimental results showed that DEACD has very competitive performance compared with other state-of-the-art community detection algorithms. In the process of evolution, the colony evolution was conducted under DE scheme, the network modularity was used to evaluate the fitness of individuals in the colony. The performance of DECD was analyzed by computer generated network and real-world network examples. The algorithm was implemented using matlab Genetic algorithm Optimization Toolbox (GAOT), and the parametric analysis was performed in the experiment.

关键词： Complex Network differential evolution algorithm Community Detection

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differential evolution algorithm applied to sidelobe level reduction on a planar array

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AEU-INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATIONS 2007年第5期61卷 286-290页

作者： Rocha-Alicano, Carlos Covarrubias-Rosales, David Brizuela-Rodriguez, Carlos Panduro-Mendoza, Marco CICESE Res Ctr Ensenada 22860 Baja California Mexico Autonomous Univ Tamaulipas Reynosa Rhode Acad Ctr Reynosa Mexico

In this paper, differential evolution (DE), one of the youngest paradigms in evolutionary computation, is applied to the process of synthesizing a planar array factor focused on sidelobe-level reduction. Sidelobe level is a critical array factor parameter in the task of reducing background noise and interference in the most recent Wireless Communications Systems. Minimization of sidelobe level involves nonlinear and non-convex dependence between array factor and its elements parameters becoming a highly complex problem. However, DE has proven to be a fast and efficient algorithm for complex real-valued problems. Subsequently, a binary-coded genetic algorithm is proposed for the synthesis of planar arrays. Numerical results show a promising performance ofDE reducing noticeably the sidelobe level generated by a uniform planar array. (C) 2006 Elsevier GmbH. All rights reserved.

关键词： planar array differential evolution algorithm array factor synthesis

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