An Improved NSGA-III Algorithm Using Genetic K-Means Clustering Algorithm

Liu, Qingguo; Liu, Xinxue; Wu, Jian; Li, Yaxiong

doi:10.1109/access.2019.2960531

Cited by 38 publications

(21 citation statements)

References 34 publications

(29 reference statements)

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“…The nondominated sorting genetic algorithm III (NSGA-III) [21][22][23] has been successfully adopted to solve various multiobjective optimisation problems, and variants have been developed for specific problems [24][25][26][27][28]. Bhesdadiya et al [21] applied NSGA-III to lower the emission value and fuel cost of fossil fuel power plants while providing a sustainable and reliable power supply.…”

Section: Main Transmission Gearbox (Mtg)mentioning

confidence: 99%

“…Bi and Wang [26] proposed an improved NSGA-III with enhanced convergence. Liu et al [27] improved NSGA-III using a genetic K-means (GKM) clustering algorithm to improve convergence. Zheng et al [28] introduced the acceleration of the differential evolution mechanism for improving NSGA-III to accelerate convergence and diversity exploration.…”

Section: Main Transmission Gearbox (Mtg)mentioning

confidence: 99%

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Multiobjective Multistate System Preventive Maintenance Model with Human Reliability

Huang

Wang

Chen

2021

International Journal of Aerospace Engineering

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Modern equipment is designed to operate under deteriorating performance conditions resulting from internal ageing and/or external environmental impacts influencing downstream maintenance. This study focuses on the development of a multistate system (MSS) that considers a human reliability factor associated with maintenance personnel—a condition-based multiobjective MSS preventive maintenance model (MSSPMM). The study assumes that no more than one maintenance activity is performed to achieve the most appropriate preventive maintenance (PM) strategy and easy implementation and to reduce maintenance error due to human reliability. The MSS performance based on mean system unavailability and total maintenance cost is evaluated using a stochastic model approach, and then, the MSSPMM is used for optimisation. A customised version of the nondominated sorting genetic algorithm III is employed to ensure efficient solution of the PM model with human reliability—which is considered a constrained multiobjective combinatorial optimisation problem. The optimised solutions are determined from the nondominated Pareto frontier comprising the diversified PM alternatives. A helicopter power transmission system is used as an example to illustrate the efficacy and applicability of the proposed approach through sensitivity analyses with relevant parameters.

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Section: Main Transmission Gearbox (Mtg)mentioning

confidence: 99%

Section: Main Transmission Gearbox (Mtg)mentioning

confidence: 99%

Multiobjective Multistate System Preventive Maintenance Model with Human Reliability

Huang

Wang

Chen

2021

International Journal of Aerospace Engineering

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“…Scholars at home and abroad have proposed various strategies for the optimization of distributed database queries. Examples include SDD-1 [2-3], dynamic programming [4][5], simulated annealing [6][7], genetic algorithm [8][9][10] and so on. Paper [11] proposed a query optimization method based on the Tabu-GEP algorithm, which combines the Tabu search strategy with the GEP algorithm.…”

Section: Introductionmentioning

confidence: 99%

Optimization Method for Distributed Database Query Based on an Adaptive Double Entropy Genetic Algorithm

Zheng

Li²,

Tian

et al. 2022

IEEE Access

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In a distributed database environment, multi-join query optimization is one of the key factors affecting database performance. Genetic algorithms have a good application in dealing with this type of problem. However, the traditional genetic algorithm has the problems of low efficiency and easily falls into the precocity when dealing with query optimization, which is mainly caused by the lack of population diversity. Therefore, this paper sets up a mathematical model for distributed database query optimization and proposes an adaptive genetic algorithm based on double entropy. We introduced a genetic algorithm with two types of entropy: genotype and phenotype. Genotype entropy was used to optimize the distribution of the initial population, ensuring that the initial population has good population diversity. Phenotype entropy is used to optimize the genetic strategy, which can be divided into individual entropy and population entropy. Individual entropy is used to optimize the selection strategy, and population entropy is used to optimize the crossover and mutation operators to maintain the population diversity in the iteration process and accelerate the speed of iteration. The experimental results show that the algorithm proposed in this paper is effective for query optimization of a distributed database.

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“…Bai et al [4] applied k-means in fast density clustering algorithm. Liu et al [5] considered the extended genetic k-means. Jung et al [6] gave a reinforce k-means for lowering data cost.…”

Section: Introductionmentioning

confidence: 99%

Entropy K-Means Clustering With Feature Reduction Under Unknown Number of Clusters

2021

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The k-means algorithm with its extensions is the most used clustering method in the literature. But, the k-means and its various extensions are generally affected by initializations with a given number of clusters. On the other hand, most of k-means always treat data points with equal importance for feature components. There are several feature-weighted k-means proposed in literature, but, these feature-weighted k-means do not give a feature reduction behavior. In this paper, based on several entropy-regularized terms we can construct a novel k-means clustering algorithm, called Entropy-k-means, such that it can be free of initializations without a given number of clusters, and also has a feature reduction behavior. That is, the proposed Entropy-k-means algorithm can eliminate irrelevant features with feature reduction under free of initializations with automatically finding an optimal number of clusters. Comparisons between the proposed Entropy-k-means and other methods are made. Experimental results and comparisons actually demonstrate these good aspects of the proposed Entropy-k-means with its effectiveness and usefulness in practice.

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An Improved NSGA-III Algorithm Using Genetic K-Means Clustering Algorithm

Cited by 38 publications

References 34 publications

Multiobjective Multistate System Preventive Maintenance Model with Human Reliability

Multiobjective Multistate System Preventive Maintenance Model with Human Reliability

Optimization Method for Distributed Database Query Based on an Adaptive Double Entropy Genetic Algorithm

Entropy K-Means Clustering With Feature Reduction Under Unknown Number of Clusters

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