Clustering is a prominent research area, with numerous studies and the development of hundreds of algorithms over the years. However, a fundamental challenge in clustering research is the trade-off between algorithm s...
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Clustering is a prominent research area, with numerous studies and the development of hundreds of algorithms over the years. However, a fundamental challenge in clustering research is the trade-off between algorithm speed and clustering quality. Existing algorithms tend to prioritize either fast execution with compromised clustering quality or slower performance with superior clustering results. In this study, we propose a novel CDC-2 algorithm, an improved version of the Critical Distance Clustering (CDC) algorithm, to address this challenge. Inspired by the concepts of hybridization in biology and the division of labor in the economic system, we present a new hybridization strategy. Our approach integrates the connectivity and coherence aspects of the K-means and CDC-2 algorithms, respectively, allowing us to combine speed and quality in a single algorithm. This approach is referred to as the CDC++ algorithm, and it is characterized as a hybrid that combines elements from two algorithms, K-means and CDC-2, in order to leverage their strengths while mitigating their weaknesses. Moreover, the structure and mechanism of the CDC++ algorithm led to the introduction of a new concept called "object autoencoder." Unlike traditional feature reduction methods, this concept focuses on object reduction, representing a significant advancement in clustering techniques. To validate our approach, we conducted experimental studies on thirteen synthetic and five real datasets. Comparative analysis with four well-known algorithms demonstrates that our proposed development and hybridization enable efficient processing of largescale and high-dimensional datasets without compromising clustering quality.
In the domain of artificial neural networks,the learning process represents one of the most challenging *** the classification accuracy highly depends on theweights and biases,it is crucial to find its optimal or subo...
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In the domain of artificial neural networks,the learning process represents one of the most challenging *** the classification accuracy highly depends on theweights and biases,it is crucial to find its optimal or suboptimal values for the problem at ***,to a very large search space,it is very difficult to find the proper values of connection weights and *** traditional optimization algorithms for this issue leads to slow convergence and it is prone to get stuck in the local *** commonly,back-propagation is used formulti-layer-perceptron training and it can lead to vanishing gradient *** an alternative approach,stochastic optimization algorithms,such as nature-inspired metaheuristics are more reliable for complex optimization tax,such as finding the proper values of weights and biases for neural network *** thiswork,we propose an enhanced brain storm optimization-based algorithm for training neural *** the simulations,ten binary classification benchmark datasets with different difficulty levels are used to evaluate the efficiency of the proposed enhanced brain storm optimization *** results show that the proposed approach is very promising in this domain and it achieved better results than other state-of-theart approaches on the majority of datasets in terms of classification accuracy and convergence speed,due to the capability of balancing the intensification and diversification and avoiding the local *** proposed approach obtained the best accuracy on eight out of ten observed dataset,outperforming all other algorithms by 1-2%on *** mean accuracy is observed,the proposed algorithm dominated on nine out of ten datasets.
With an increasingly wider application of Phasor Measurement Units (PMUs), the accuracy of phasor parameter estimation has become one of the major concerns in related research. The accuracy of phasor parameter estimat...
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ISBN:
(纸本)9781538682326
With an increasingly wider application of Phasor Measurement Units (PMUs), the accuracy of phasor parameter estimation has become one of the major concerns in related research. The accuracy of phasor parameter estimation is closely associated with the accuracy of waveform model representation. The most commonly used waveform models in literature are Fourier models, and Taylor polynomial models, which are the backbones of frequency-domain and time-domain phasor estimation approaches, respectively. Usually, neither solution alone is capable of producing satisfactory results, despite various improvement strategies. This paper proposes a novel method for hybridization of existing Fourier and polynomial fitting-based estimation methods. The framework is introduced so that the merits from multiple algorithms can be leveraged without designing a completely new algorithm. Fourier method and Taylor expansion method are selected to demonstrate this approach. Both theoretical derivation and simulation results show that the proposed framework effectively integrates the benefits from both algorithms to achieve sufficient accuracy.
Cooperative co-evolution (CC) is an effective framework that can be used to solve large-scale optimization problems. It typically divides a problem into components and uses one optimizer to solve the components in a r...
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ISBN:
(纸本)9781450356183
Cooperative co-evolution (CC) is an effective framework that can be used to solve large-scale optimization problems. It typically divides a problem into components and uses one optimizer to solve the components in a round-robin fashion. However the relative contribution of each component to the overall fitness value may vary. Furthermore, using one optimizer may not be sufficient when solving a wide range of components with different characteristics. In this paper, we propose a novel CC framework which can select an appropriate optimizer to solve a component based on its contribution to the fitness improvement. In each evolutionary cycle, the candidate optimizer and component that make the greatest contribution to the fitness improvement are selected for evolving. We evaluated the efficacy of the proposed CC with Optimizer Selection (CCOS) algorithm using large-scale benchmark problems. The numerical experiments showed that CCOS outperformed the CC model without optimizer selection ability. When compared against several other state-of-the-art algorithms, CCOS generated competitive solution quality.
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