Fuzzy c-means clustering-based mating restriction for multiobjective optimization

Zhang, Yi; Li, Zimu; Zhang, Hu; Yu, Zhen; Lu, Tong-tong

doi:10.1007/s13042-017-0668-6

Cited by 16 publications

(4 citation statements)

References 29 publications

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“…where z i is the i-th element of clustered data, v j is a two-dimensional vector with the coordinates of the j-th cluster, m is the fuzzification coefficient, µ ij is the degree of membership of z i to the j-th cluster, • is any norm to evaluate the similarity between a data point and a centroid [35].…”

Section: Concepts Extractionmentioning

confidence: 99%

Measuring Wind Turbine Health Using Drifting Concepts

Jastrzębska¹,

Morales-Hernández²,

Nápoles³

et al. 2021

Preprint

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Time series processing is an essential aspect of wind turbine health monitoring. Despite the progress in this field, there is still room for new methods to improve modeling quality. In this paper, we propose two new approaches for the analysis of wind turbine health. Both approaches are based on abstract concepts, implemented using fuzzy sets, which summarize and aggregate the underlying raw data. By observing the change in concepts, we infer about the change in the turbine's health. Analyzes are carried out separately for different external conditions (wind speed and temperature). We extract concepts that represent relative low, moderate, and high power production. The first method aims at evaluating the decrease or increase in relatively high and low power production. This task is performed using a regression-like model. The second method evaluates the overall drift of the extracted concepts. Large

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Section: Concepts Extractionmentioning

confidence: 99%

Measuring Wind Turbine Health Using Drifting Concepts

Jastrzębska¹,

Morales-Hernández²,

Nápoles³

et al. 2021

Preprint

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show abstract

“…This allows the algorithm to complete the category update operations of adding individuals and deleting inferior solutions while selecting the environment. In addition, there are some multi-objective reproduction operators constructed from other perspectives [34][35][36].…”

Section: Clustering Based Reproduction Operatorsmentioning

confidence: 99%

A Multi-Objective Evolutionary Algorithm Based on KNN-Graph for Traffic Network Attack

Wang

Zhang

et al. 2020

Electronics

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The research of vulnerability in complex network plays a key role in many real-world applications. However, most of existing work focuses on some static topological indexes of vulnerability and ignores the network functions. This paper addresses the network attack problems by considering both the topological and the functional indexes. Firstly, a network attack problem is converted into a multi-objective optimization network vulnerability problem (MONVP). Secondly to deal with MONVPs, a multi-objective evolutionary algorithm is proposed. In the new approach, a k-nearest-neighbor graph method is used to extract the structure of the Pareto set. With the obtained structure, similar parent solutions are chosen to generate offspring solutions. The statistical experiments on some benchmark problems demonstrate that the new approach shows higher search efficiency than some compared algorithms. Furthermore, the experiments on a subway system also suggests that the multi-objective optimization model can help to achieve better attach plans than the model that only considers a single index.

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“…Clustering methods are used to learn the population structure information to define the neighborhood relationship among solutions, and neighbor based mating reproductions are performed for local exploitation to improve the convergence of algorithms. Self-organizing map, k-means and spectral clustering are commonly used clustering approaches [6], [36], [37], [57].…”

Section: Introductionmentioning

confidence: 99%

Combining Global and Local Information for Offspring Generation in Evolutionary Multiobjective Optimization

Long¹,

Liu²,

Jin³

2021

IEEE Access

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The Pareto optimal set of a continuous multiobjective optimization problem shows some kinds of structure, which is called the regularity property and it has been applied to design effective offspring reproduction operators in multiobjective optimization evolutionary algorithms (MOEAs). Usually, the probability model sampling and neighbor based mating are the main strategies to implement the regularity property in designing reproduction operators. In fact, different information is used in these methods. There is no doubt that combining the global and local information will favor the search. To use more information in the offspring reproduction, in this paper, we propose a regularity assisted MOEA, RAMEA for short, that combines Gaussian sampling and neighbor based mating for offspring reproduction. In RAMEA, the k-means clustering method is used to learn the manifold structure information and partition the population into K clusters. A Gaussian probability model is built with K mean vectors of clusters, and K trial offspring solutions are sampled from thus model. Moreover, these sampled trial solutions are added to each cluster as mating parents to generate other offspring solutions. In this way, the global and local information are combined to generate offspring solutions in RAMEA. The proposed approach has been executed in several test instances with complicated characteristics, and compared with seven classical or newly developed MOEAs. The results have demonstrated its advantages over other algorithms.

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Fuzzy c-means clustering-based mating restriction for multiobjective optimization

Cited by 16 publications

References 29 publications

Measuring Wind Turbine Health Using Drifting Concepts

Measuring Wind Turbine Health Using Drifting Concepts

A Multi-Objective Evolutionary Algorithm Based on KNN-Graph for Traffic Network Attack

Combining Global and Local Information for Offspring Generation in Evolutionary Multiobjective Optimization

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