Resolution capability of any optical imaging system is limited by residual aberrations as well as diffraction effects. Overcoming this fundamental limit is called super-resolution. Several new paradigms for super-reso...
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Resolution capability of any optical imaging system is limited by residual aberrations as well as diffraction effects. Overcoming this fundamental limit is called super-resolution. Several new paradigms for super-resolution in optical systems use 'a posteriori' digital image processing. In these ventures the three-dimensional point spread function (PSF) of the lens plays a key role in image acquisition. A straightforward tailoring of the PSF can be performed by appropriate pupil plane filtering. With a brief review of the state-of-art in this research area, this paper dwells upon the inverse problem of global optimization of the pupil function by phase filtering in accordance with the desired PSF.
Distribution Network Reconfiguration (DNR) is required to identify the best topology network in order to fulfill the power demand with minimum power losses. This paper proposes a new method which is called as Rank Evo...
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ISBN:
(纸本)9781479938575
Distribution Network Reconfiguration (DNR) is required to identify the best topology network in order to fulfill the power demand with minimum power losses. This paper proposes a new method which is called as Rank evolutionary Particle Swarm Optimization (REPSO). The proposed method is a combination of the Particle Swarm Optimization (PSO) and the traditional evolutionary programming (EP) algorithm with a rejuvenation of the additional of ranking element. The main objective of this paper is to reduce the power losses while improving the convergence time. The proposed method will be implemented and the real power losses in the IEEE 33-bus test system will be investigated and analyzed accordingly. The results are compared to the conventional PSO and hybridization EPSO method and it is hoped to help the power system engineer in securing the network with the less power loss in the future.
Typically, consumers experience power outages as a result of transmission system issues. Transmission lines are one of the most used technical systems for transporting large amounts of electricity from one part of a c...
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ISBN:
(纸本)9781665401937
Typically, consumers experience power outages as a result of transmission system issues. Transmission lines are one of the most used technical systems for transporting large amounts of electricity from one part of a country to the farthest point in the opposite direction. The transmission lines traverse many terrain types and geographic regions, which makes them more sensitive to various types of atmospheric disasters and causes frequent line faults and power outages. Therefore, power system reliability and power loss are important, since if these losses are higher than the industry norm, the amount of power accessible to customers will be reduced. Power from a distributed generator (DG) system is incorporated into a power system in this study to improve the power system reliability, average energy not supplied (AENS), and reduce transmission line losses. The results of an optimal DG installation are gained from the penetration of distributed generation in an IEEE 14-bus system by using the evolutionary programming (EP) optimization technique. The results prove that with the use of EP optimization, power system reliability can be improved post optimization.
Organizations across the globe gather more and more data. Large datasets require new approaches to analysis and processing, which include methods based on machine learning. In particular, the symbolic regression can p...
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ISBN:
(纸本)9783319321493;9783319321486
Organizations across the globe gather more and more data. Large datasets require new approaches to analysis and processing, which include methods based on machine learning. In particular, the symbolic regression can provide many useful insights. Unfortunately, due to high resource requirements, the use of this method for large datasets might be unfeasible. In this paper we analyze a bottleneck in an open-source implementation of this method, we call hubert. We identify that the evaluation of individuals is the most costly operation. As a solution to this problem, we propose a new evaluation service based on the Apache Spark framework, which attempts to speed up computations by distributing them on a cluster of machines. We compare the performance of the service by analyzing the execution time for a number of samples with use of both implementations. Then we discuss how the computation time improves with increased amount of resources. Finally we draw conclusions and outline plans for further research.
This paper focuses on question paper template generation and its use in dynamic generation of examination question paper. Question paper template generation is a constrained based optimization problem. Choosing an eff...
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ISBN:
(纸本)9780769547596;9781467321730
This paper focuses on question paper template generation and its use in dynamic generation of examination question paper. Question paper template generation is a constrained based optimization problem. Choosing an efficient, scientific and rational algorithm to generate a template is the key to dynamic examination question paper generation. By using the evolutionary computational search technique of evolutionary programming and educational taxonomies, this paper analyses and experimentally proves that the generated question paper templates are best suited for dynamic examination paper generation. This new technique outperforms traditional algorithms in terms of coverage of topics, learning domains and marks distribution in the generated question paper.
Chaotic time series have been successfully predicted with the EPNet algorithm through the evolution of artificial neural networks. However, the input feature selection problem has either not been fully explored before...
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ISBN:
(纸本)9780769545639
Chaotic time series have been successfully predicted with the EPNet algorithm through the evolution of artificial neural networks. However, the input feature selection problem has either not been fully explored before or has not been compared against other algorithms in the literature. This paper presents four algorithms derived from the classical EPNet algorithm to evolve the input feature selection in three different chaotic series: Logistic, Lorenz and Mackey-Glass. Additionally, some flaws in the prediction field that may be considered in future works are discussed. A comparison against previous work demonstrates that in most cases the specialization of the EPNet algorithm allows better solutions with a smaller number of generations.
作者：
Coello, CACBecerra, RLIPN
CINVESTAV Colutionary Computat Grp Dept Ing ElectSecc Computac Mexico City 07300 DF Mexico
In this paper, we present the first proposal to use a cultural algorithm to solve multiobjective optimization problems. Our proposal uses, evolutionary programming, Pareto ranking and elitism (i.e., an external popula...
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ISBN:
(纸本)0780379144
In this paper, we present the first proposal to use a cultural algorithm to solve multiobjective optimization problems. Our proposal uses, evolutionary programming, Pareto ranking and elitism (i.e., an external population). The approach proposed is validated using several examples taken from the specialized literature. Our results are compared with respect to the NSCA-II, which is an algorithm representative of the state-of-the-art in evolutionary multiobjective optimization. The performance of our approach indicates that cultural algorithms are a viable alternative for multiobjective optimization.
An adaptive grouping technique for a cooperative coevolutionary model is presented in this paper. The coevolutionary model is based on Monte-Carlo (MC) simulations and evolutionary programming to solve the balancing b...
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ISBN:
(纸本)9781509054879
An adaptive grouping technique for a cooperative coevolutionary model is presented in this paper. The coevolutionary model is based on Monte-Carlo (MC) simulations and evolutionary programming to solve the balancing between exploration and exploitation in coevolution. The proposed model decomposes a large scale optimization problem into sub-problems of varying sizes. The coevoluationary model with a new grouping technique is applied on a retail dataset for sales optimization. The model is also evaluated using two multidimensional benchmark functions. The results indicate a clear advantage of using Monte-Carlo simulations to balance the trade-off between exploration and exploitation in large scale and high dimensional problem spaces.
Although the Linux2.6 kernel contains a O(1) scheduler, it does not include a certain algorithm to analyze the task's total execution time, which will directly result in certain shortcomings in the computing speed...
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ISBN:
(纸本)9781457715846
Although the Linux2.6 kernel contains a O(1) scheduler, it does not include a certain algorithm to analyze the task's total execution time, which will directly result in certain shortcomings in the computing speed. In order to minimize the task scheduling time of completing load balance, this paper introduced the EP algorithm (evolutionary programming) applied in Scheduler. When the system needs to migration process to achieve the load balance, the improved scheduler analyzes the task of busy CPU queue as an entity and then finds out the most optimal distribution solution through the global search ability of EP algorithm, which will eventually achieve the shortest total execution time. The experiment results demonstrate that the enhanced algorithm is more advanced in computing speed.
In this paper a new hybrid model has been proposed for cost analysis of real as well reactive power wheeling under deregulated environment of modern power system. The proposed hybrid model is mainly an integration of ...
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ISBN:
(纸本)0780381106
In this paper a new hybrid model has been proposed for cost analysis of real as well reactive power wheeling under deregulated environment of modern power system. The proposed hybrid model is mainly an integration of a classical approach i.e. steepest decent method (SD) and artificial intelligence approach i.e. evolutionary programming (EP) based optimal power flow (OPF). Previously wheeling cost was analyzed using classical techniques like SD but they are bound to handle generating units with differentiable cost characteristic only. Presently it has been observed that under deregulated environment, some of the generating units e.g. co-generation plants have non-differentiable cost characteristics. So, for calculation and analysis of wheeling cost under present day modern deregulated environment, the proposed hybrid model is found to be most suitable. This new hybrid model is first tested on IEEE 30-bus test system and then applied to modified IEEE 30-bus test system. The results so obtained are found to be quite encouraging.
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