Wavelet based compression is mostly utilized to compress the videos. Most of the wavelet-based compression techniques first determines the coefficients and then performs a threshold-based operation to obtain the compr...
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ISBN:
(纸本)9781467313421
Wavelet based compression is mostly utilized to compress the videos. Most of the wavelet-based compression techniques first determines the coefficients and then performs a threshold-based operation to obtain the compressed video. In such works, there is a lack of analysis in selecting an appropriate threshold value. To overcome such drawback presented in the existing methods, we have proposed a video compression technique with an adaptive threshold selection method to compress the videos in this paper. The simulation results show the effectiveness of proposed video compression technique. The performance of the video compression technique is evaluated by comparing the result of proposed technique with the existing video compression technique. The comparison result shows that the enhanced quality of the image as well as the compression ratio of the proposed technique.
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 se...
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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.
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 al...
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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.
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 netw...
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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.
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 ...
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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.
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 lo...
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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.
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....
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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.
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 a...
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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.
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 pape...
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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.
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 schedul...
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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.
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