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57th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE)

作者： Georgiev, Milen Tanev, Ivan Shimohara, Katsunori Doshisha Univ Grad Sch Sci & Engn 1-3 Tatara Miyakodani Kyotanabe Kyoto 6100321 Japan

ISBN: (纸本)9784907764609

The purpose of this research is to investigate the performance of heterogeneous multi-agent systems of agents in comparison to morphologically identical homogeneous systems, pertaining the same average physical and sensory abilities for the system as a whole. We will be using a form of the well-known predator-prey pursuit problem to measure the efficiency of each of the systems in both speed of evolution of the exhibited behavior and robustness of the programmatically generated solutions.

关键词： multi-agent systems predator-prey pursuit problem evolutionary programming heterogeneous and homogeneous system performance comparison and analysis

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Exploiting Building Blocks in Hard Problems with Modified Compact Genetic Algorithm 15

Exploiting Building Blocks in Hard Problems with Modified Co...

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15th International Joint Conference on Computer Science and Software Engineering (JCSSE)

作者： Suksen, Kamonluk Chongstitvatana, Prabhas Chulalongkorn Univ Fac Engn Dept Comp Engn Bangkok Thailand

ISBN: (纸本)9781538655382

In evolutionary Computation, good substructures that are combined into good solutions are called building blocks. In this context, building blocks are common structure of high-quality solutions. The compact genetic algorithm is an extension of the genetic algorithm that replaces the latter's population of chromosomes with a probability distribution from which candidate solutions can be generated. This paper describes an algorithm that exploits building blocks with compact genetic algorithm in order to solve difficult optimization problems under the assumption that we have already known building blocks. The main idea is to update the probability vectors as a group of bits that represents building blocks thus avoiding the disruption of the building blocks. Comparisons of the new algorithm with a conventional compact genetic algorithm on trap-function and traveling salesman problems indicate the utility of the proposed algorithm. It is most effective when the problem instants have common structures that can be identify as building blocks.

关键词： genetic algorithm genetic programming combinatorial optimization evolutionary programming

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AC transmission network expansion planning considering losses

AC transmission network expansion planning considering losse...

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IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe)

作者： Morquecho, Edgar G. Torres, Santiago P. Espinoza, Juan L. Lopez, Julio C. Quizhpe, Klever L. Sempertegui, Rodrigo E. Solano, Lizandro D. Univ Cuenca Dept Elect Elect & Telecommun Engn Cuenca Ecuador

ISBN: (纸本)9781538645055

This paper proposes to solve the transmission network expansion planning problem (TNEP) using the AC model formulated with full non-linear load flow equations, incorporating the cost of losses in the transmission network. Additionally, the decomposed formulation finds the location and amount of the reactive compensation needed in the system. A comparison between evolutionary programming (EP) and a variation of EP with a Cultural Algorithm (CEP) is presented to solve this very complex optimization problem. The results are obtained using Garver's 6-bus test system and IEEE 24-bus test system.

关键词： AC model Cultural Algorithm evolutionary programming Optimization Transmission network expansion planning

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Distribution Network Reconfiguration for Voltage Stability Enhancement via Feasibility-Preserving evolutionary Optimization

Distribution Network Reconfiguration for Voltage Stability E...

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IEEE Electrical Power and Energy Conference (EPEC)

作者： Rojas, Alberto Landeros Koziel, Slawomir Abdel-Fattah, Mohamed F. Gutierrez-Alcaraz, Guillermo Reykjavik Univ Engn Optimizat & Modeling Ctr Reykjavik Iceland PGIIE Inst Tecnol Morelia Dept Elect Engn Morelia Michoacan Mexico

ISBN: (纸本)9781538654194

This paper addresses a problem of power distribution network optimization, specifically, an improvement of the voltage stability index (VSI). A customized algorithm, referred to as feasibility preserving evolutionary optimization (FPEO), is developed and employed that involves recombination and mutation operators tailored to maintain the radial architecture of the network and enhances VSI through network reconfiguration. The algorithm performance is validated using the three standard test distribution systems, 33-, 69-, and 119-bus networks. Excellent repeatability of results is achieved as demonstrated through comprehensive statistical analysis. The optimal reconfiguration found by the proposed algorithm significantly improves the VSI of the buses most prone to voltage collapse. At the same time, the voltage profile improvement is obtained along with reduction of the power losses.

关键词： Distribution network reconfiguration voltage stability index evolutionary programming feasibility preserving power systems radial networks smart grid

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Co-evolution of Fitness Predictors and Deep Neural Networks 12th

Co-evolution of Fitness Predictors and Deep Neural Networks

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12th International Conference on Parallel Processing and Applied Mathematics (PPAM)

作者： Funika, Wlodzimierz Koperek, Pawel AGH Univ Sci & Technol Fac Comp Sci Elect & Telecommun Dept Comp Sci Al Mickiewicza 30 PL-30059 Krakow Poland

ISBN: (纸本)9783319780245;9783319780238

Deep neural networks proved to be a very useful and powerful tool with many applications. In order to achieve good learning results, the network architecture has, however, to be carefully designed, which requires a lot of experience and knowledge. Using an evolutionary process to develop new network topologies can facilitate this process. The limiting factor is the speed of evaluation of a single specimen (a single network architecture), which includes learning based on a large dataset. In this paper we propose a new approach which uses subsets of the original training set to approximate the fitness. We describe a co-evolutionary algorithm and discuss its key elements. Finally we draw conclusions from experiments and outline plans for future work.

关键词： evolutionary programming Genetic programming Deep neural networks Neural networks Co-evolution Fitness predictors

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Data Clustering and Image Segmentation Through Genetic Algorithms: Emerging Research and Opportunities

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2018年

作者： S. Dash B.K. Tripathy

ISBN: (数字)9781522563204

ISBN: (纸本)9781522563198;1522563199

As computers are being used more and more to solve complex problems, the application of biology or natural evolution principles to the study and design of human systems helps provide efficient optimization algorithms. Data Clustering and Image Segmentation Through Genetic Algorithms: Emerging Research and Opportunities is an essential reference source that discusses applications of bio-inspired algorithms in data mining, computer vision, image processing, and pattern recognition, as well as methods of designing competent algorithms based on decomposition principles. Featuring research on topics such as cluster analysis, metaheuristic optimization, and image processing, this book is ideally designed for IT professionals, computer engineers, researchers, academicians, and upper-level students seeking coverage on how to develop efficient clustering algorithms.

关键词： Cluster Analysis evolutionary Algorithms evolutionary programming Image Processing Metaheuristic Optimization Natural Genetics Nature-Inspired Algorithms Partitioning Methods

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An improved FCMBP fuzzy clustering method based on evolutionary programming

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COMPUTERS & MATHEMATICS WITH APPLICATIONS 2011年第4期61卷 1129-1144页

作者： Tan, Qing He, Qing Zhao, Weizhong Shi, Zhongzhi Lee, E. S. Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc Beijing 100190 Peoples R China Chinese Acad Sci Grad Sch Beijing 100049 Peoples R China Kansas State Univ Dept Ind Engn Manhattan KS 66506 USA

In current PC computing environment, the fuzzy clustering method based on perturbation (FCMBP) is failed when dealing with similar matrices whose orders are higher than tens. The reason is that the traversal process adopted in FCMBP is exponential complexity. This paper treated the process of finding fuzzy equivalent matrices with smallest error from an optimization point of view and proposed an improved FCMBP fuzzy clustering method based on evolutionary programming. The method seeks the optimal fuzzy equivalent matrix which is nearest to the given fuzzy similar matrix by evolving a population of candidate solutions over a number of generations. A new population is formed from an existing population through the use of a mutation operator. Better solutions survive into next generation and finally the globally optimal fuzzy equivalent matrix could be obtained or approximately obtained. Compared with FCMBP, the improved method has the following advantages: (1) Traversal searching is avoided by introducing an evolutionary programming based optimization technique. (2) For low-order matrices, the method has much better efficiency in finding the globally optimal fuzzy equivalent matrix. (3) Matrices with hundreds of orders could be managed. The method could quickly get a more accurate solution than that obtained by the transitive closure method and higher precision requirement could be achieved by further iterations. And the method is adaptable for matrices of higher order. (4) The method is robust and not sensitive to parameters. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Fuzzy clustering FCMBP fuzzy clustering Optimal fuzzy equivalent matrix evolutionary programming

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OBSERVER-BASED ITERATIVE LEARNING CONTROL WITH evolutionary programming ALGORITHM FOR MIMO NONLINEAR SYSTEMS

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INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2011年第3期7卷 1357-1374页

作者： Du, Yan-Yi Tsai, Jason Sheng-Hong Guo, Shu-Mei Su, Te-Jen Chen, Chia-Wei Natl Cheng Kung Univ Dept Elect Engn Tainan 70101 Taiwan Natl Cheng Kung Univ Dept Comp Sci & Informat Engn Tainan 70101 Taiwan Kun Shan Univ Coll Informat Technol Tainan 710 Taiwan Kao Yuan Univ Dept Mech & Automat Engn Kaohsiung 821 Taiwan

In this paper, the observer-based iterative learning control with/without evolutionary programming algorithm is proposed for MIMO nonlinear systems. While the learning gain involves some immeasurable states, this paper proposes the observer-based iterative learning control (ILC) for nonlinear systems and guarantees the tracking error convergences to zero via continual learning. Moreover, a sufficient condition has been presented to alleviate the traditional constraint, i.e., identical initial state, in the convergence analysis. Then, an idea of feasible reference based on polynomial approximation is proposed to overcome the limitation of ILC - initial state error. To speed up the convergence of the iterative learning control, evolutionary programming is applied to search for the optimal and feasible learning gain to reduce the training aim. In addition, two improved issues of ILC, an appropriate selection of the initial control input and the improved learning rule for the system whose product matrix of output matrix C and input matrix B is not full rank, are presented in this paper. Three multi-input multi-output (MIMO) illustrative examples are presented to demonstrate the effectiveness of the proposed methodology.

关键词： Iterative learning control D-type ILC Observer-based ILC evolutionary programming

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Hydro-thermal unit commitment problem using simulated annealing embedded evolutionary programming approach

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2011年第4期33卷 939-946页

作者： Rajan, C. Christober Asir Pondicherry Engn Coll Dept EEE Pondicherry 605014 India

This paper presents a new approach to solve the hydro-thermal unit commitment problem using Simulated Annealing embedded evolutionary programming approach. The objective of this paper is to find the generation scheduling such that the total operating cost can be minimized, when subjected to a variety of constraints. A utility power system with 11 generating units in India demonstrates the effectiveness of the proposed approach;extensive studies have also been performed for different IEEE test systems consist of 25, 44 and 65 units. Numerical results are shown comparing the cost solutions and computation time obtained by conventional methods. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Hydro-thermal unit commitment evolutionary programming Simulated annealing

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Influence of the number and location of design parameters in the aerodynamic shape optimization of a transonic aerofoil and a wing through evolutionary algorithms and support vector machines

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ENGINEERING OPTIMIZATION 2017年第2期49卷 181-198页

作者： Andres-Perez, Esther Gonzalez-Juarez, Daniel Martin-Burgos, Mario J. Carro-Calvo, Leopoldo Salcedo-Sanz, Sancho Ingn Sistemas Def Espana SA ISDEFE INTA Dept Engn Torrejon De Ardoz Madrid Spain Tech Univ Madrid UPM Torrejon De Ardoz Madrid Spain Spanish Natl Inst Aerosp Technol INTA Fluid Dynam Branch Torrejon De Ardoz Madrid Spain Univ Alcala UAH Dept Signal Theory & Commun Alcala De Henares Spain

Surrogate-based optimization (SBO) has recently found widespread use in aerodynamic shape design owing to its promising potential to speed up the whole process by the use of a low-cost objective function evaluation, to reduce the required number of expensive computational fluid dynamics simulations. However, the application of these SBO methods for industrial configurations still faces several challenges. The most crucial challenge nowadays is the 'curse of dimensionality', the ability of surrogates to handle a high number of design parameters. This article presents an application study on how the number and location of design variables may affect the surrogate-based design process and aims to draw conclusions on their ability to provide optimal shapes in an efficient manner. To do so, an optimization framework based on the combined use of a surrogate modelling technique (support vector machines for regression), an evolutionary algorithm and a volumetric non-uniform rational B-splines parameterization are applied to the shape optimization of a two-dimensional aerofoil (RAE 2822) and a three-dimensional wing (DPW) in transonic flow conditions.

关键词： Surrogate-based optimization aerodynamic shape design evolutionary programming support vector machines

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