Applying evolutionary optimization algorithms for improving fuzzy C-mean clustering performance to predict the deformation modulus of rock mass

Majdi, Abbas; Beiki, Morteza

doi:10.1016/j.ijrmms.2018.10.030

Cited by 30 publications

(10 citation statements)

References 41 publications

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“…At the University of Michigan, John and Bagley first proposed the genetic algorithm (GA), an optimization algorithm based on genetics and evolution theory 48 . GA is widely used in the field of optimization and optimal solutions.…”

Section: Support Vector Machines and Heuristic Algorithmsmentioning

confidence: 99%

Rock mass classification prediction model using heuristic algorithms and support vector machines: a case study of Chambishi copper mine

Zhou

et al. 2022

Sci Rep

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The rock mass is one of the key parameters in engineering design. Accurate rock mass classification is also essential to ensure operational safety. Over the past decades, various models have been proposed to evaluate and predict rock mass. Among these models, artificial intelligence (AI) based models are becoming more popular due to their outstanding prediction results and generalization ability for multiinfluential factors. In order to develop an easy-to-use rock mass classification model, support vector machine (SVM) techniques are adopted as the basic prediction tools, and three types of optimization algorithms, i.e., particle swarm optimization (PSO), genetic algorithm (GA) and grey wolf optimization (GWO), are implemented to improve the prediction classification and optimize the hyper-parameters. A database was assembled, consisting of 80 sets of real engineering data, involving four influencing factors. The three combined models are compared in accuracy, precision, recall, F1 value and computational time. The results reveal that among three models, the GWO-SVC-based model shows the best classification performance by training. The accuracy of training and testing sets of GWO-SVC are 90.6250% (58/64) and 93.7500% (15/16), respectively. For Grades I, II, III, IV and V, the precision value is 1, 0.93, 0.90, 0.92, 0.83, the recall value is 1, 1, 0.93, 0.73, 0.83, and the F1 value is 1, 0.96, 0.92, 0.81, 0.83, respectively. Sensitivity analysis is performed to understand the influence of input parameters on rock mass classification. It shows that the sensitive factor in rock mass quality is the RQD. Finally, the GWO-SVC is employed to assess the quality of rocks from the southeastern ore body of the Chambishi copper mine. Overall, the current study demonstrates the potential of using artificial intelligence methods in rock mass assessment, rendering far better results than the previous reports.

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Section: Support Vector Machines and Heuristic Algorithmsmentioning

confidence: 99%

Rock mass classification prediction model using heuristic algorithms and support vector machines: a case study of Chambishi copper mine

Zhou

et al. 2022

Sci Rep

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“…For instance, a user has different preferences while watching a movie with friends, and may have different interests while watching movie with family. We used FCM [ 63 ] to represent user to group associations. FCM assigns a membership value to each user corresponding to each group.…”

Section: Htgf Frameworkmentioning

confidence: 99%

“… Where v i is the i th cluster center, and μ ik ∈ M , is the membership value of user k to the cluster i . A detailed explanation of Fuzzy C-means can be found in [ 63 ].…”

Section: Proposed Modelmentioning

confidence: 99%

A hybrid group-based movie recommendation framework with overlapping memberships

et al. 2022

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Recommender Systems (RS) are widely used to help people or group of people in finding their required information amid the issue of ever-growing information overload. The existing group recommender approaches consider users to be part of a single group only, but in real life a user may be associated with multiple groups having conflicting preferences. For instance, a person may have different preferences in watching movies with friends than with family. In this paper, we address this problem by proposing a Hybrid Two-phase Group Recommender Framework (HTGF) that takes into consideration the possibility of users having simultaneous membership of multiple groups. Unlike the existing group recommender systems that use traditional methods like K-Means, Pearson correlation, and cosine similarity to form groups, we use Fuzzy C-means clustering which assigns a degree of membership to each user for each group, and then Pearson similarity is used to form groups. We demonstrate the usefulness of our proposed framework using a movies data set. The experiments were conducted on MovieLens 1M dataset where we used Neural Collaborative Filtering to recommend Top-k movies to each group. The results demonstrate that our proposed framework outperforms the traditional approaches when compared in terms of group satisfaction parameters, as well as the conventional metrics of precision, recall, and F-measure.

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“…Shukri et al [27] proposed a Multi-verse Optimizer (MVO) to optimize clustering problems which use the nature-inspired algorithms. Majdi et al [28] focused on the capability of the evolutionary computation methods namely, genetic algorithm (GA) and particle swarm optimization (PSO) in design and optimizing the fuzzy c-means clustering (FCM) structure and their applications to predict the deformation modulus of rock masses. Furthermore, to deal with the problem that several objective functions usually need to be optimized, a multitude of multi-objective evolutionary clustering algorithms [29][30][31][32][33] have appeared.…”

Section: Related Workmentioning

confidence: 99%

OPEC: Daily Load Data Analysis Based on Optimized Evolutionary Clustering

Lin

Zhao

2019

Energies

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Customers’ electricity consumption behavior can be studied from daily load data. Studying the daily load data for user behavior pattern analysis is an emerging research area in smart grid. Traditionally, the daily load data can be clustered into different clusters, to reveal the different categories of consumption. However, as user’s electricity consumption behavior changes over time, classical clustering algorithms are not suitable for tracing the changes, as they rebuild the clusters when clustering at any timestamp but never consider the relationship with the clusters in the previous state. To understand the changes of consumption behavior, we proposed an optimized evolutionary clustering (OPEC) algorithm, which optimized the existing evolutionary clustering algorithm by joining the Proper Restart (PR) Framework. OPEC relied on the basic fact that user’s energy consumption behavior would not abruptly change significantly, so the clusters would change progressively and remain similar in adjacent periods, except for an emergency. The newly added PR framework can deal with a situation where data changes dramatically in a short period of time, and where the former frameworks of evolutionary clustering do not work well. We evaluated the OPEC based on daily load data from Shanghai, China and the power load diagram data from UCI machine learning repository. We also carefully discussed the adjustment of the parameter in the optimized algorithm and gave an optimal value for reference. OPEC can be implemented to adapt to this situation and improve clustering quality. By understanding the changes of the users’ power consumption modes, we can detect abnormal power consumption behaviors, and also analyze the changing trend to improve the operations of the power system. This is significant for the regulation of peak load in the power grid. In addition, it can bring certain economic benefits to the operation of the power grid.

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Applying evolutionary optimization algorithms for improving fuzzy C-mean clustering performance to predict the deformation modulus of rock mass

Cited by 30 publications

References 41 publications

Rock mass classification prediction model using heuristic algorithms and support vector machines: a case study of Chambishi copper mine

Rock mass classification prediction model using heuristic algorithms and support vector machines: a case study of Chambishi copper mine

A hybrid group-based movie recommendation framework with overlapping memberships

OPEC: Daily Load Data Analysis Based on Optimized Evolutionary Clustering

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