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This paper presents insights into the proportions between the k-means clusters of successful Differential Evolution (DE), donor generating vectors. This is demonstrated by the high certainty that these proportions are similar - and thereby, that these cluster size proportions regularly appear. A characteristic of these proportions is that they are observed at the same specific values in different...
Load Patterns (LPs) clustering has a broad range of applications, such as tariff formulation, power system planning, load forecasting, and so on. In this paper, we develop a multi-objective version of Differential Evolution (DE) using a Pareto Tournament (PT) selection to solve the LP clustering problem. Our automatic DE LP clustering (ADE-LPC) algorithm provides an entire Pareto front, and by incorporating...
Microarray technology facilitates to monitor the expression levels of multiple genes simultaneously over a number of time points. For analyzing such microarray gene expression data, fuzzy clustering plays an important role. However, this fact has inspired us to propose a new automatic cluster detection method using modified differential evolution based fuzzy symmetric clustering technique. Here, allocation...
The invention of micro-array technology helps to keep the records containing expression values of multiple genes over different experimental conditions. Clustering is an unsupervised classification technique which is considered as an important tool for inspecting micro-array data. The characteristics of such micro-array data include uncertainty, noise and imprecision. Therefore fuzzy clustering techniques...
k-means algorithm, in spite of its computational efficiency and capacity for faster convergence has some serious drawbacks like its tendency to stick into local optima and the requirement of supplying number of cluster before execution. Our algorithm used Differential Evolution (DE) as preprocessor to overcome those bottlenecks. Experiments show that the improved version of clustering algorithm produces...
This paper introduces a Cluster-based Differential Evolution Algorithm with Heterogeneous Influence for solving complex optimization problems. The idea behind this combination is to classify the Differential Evolution population into a number of clusters using k-means clustering method and to apply different mutation strategies for the clusters. The number of clusters is changed dynamically in each...
Differential evolution (DE) has been proven to be a powerful population-based optimization algorithm, successfully used in various scientific and engineering fields. However, in DE, the search is guided by either a random vector or a local optimal vector. Inspired by the natural phenomenon of that good species usually contain good information, this paper propose a competent leaders guiding strategy...
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