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Soft computing

About: Soft computing is a research topic. Over the lifetime, 6710 publications have been published within this topic receiving 118508 citations.


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Journal ArticleDOI
TL;DR: In this paper, a dynamic generalized genetic algorithm (GDGA) was used to obtain a dynamic seed set in social networks under independent cascade models to identify influential nodes in these snapshot graphs.
Abstract: Over the recent decade, much research has been conducted in the field of social networks. The structure of these networks has been irregular, complex, and dynamic, and certain challenges such as network topology, scalability, and high computational complexities are typically evident. Because of the changes in the structure of social networks over time and the widespread diffusion of ideas, seed sets also need to change over time. Since there have been limited studies on highly dynamical changes in real networks, this research intended to address the network dynamicity in the classical influence maximization problem, which discovers a small subset of nodes in a social network and maximizes the influence spread. To this end, we used soft computing methods (i.e., a dynamic generalized genetic algorithm) in social networks under independent cascade models to obtain a dynamic seed set. We modeled several graphs in a specified timestamp through which the edges and the nodes changed within different time intervals. Attempts were made to find influential individuals in each of these graphs and maximize individuals’ influences in social networks, which could thereby lead to changes in the members of the seed set. The proposed method was evaluated using standard datasets. The results showed that due to the reduction of the search areas and competition, the proposed method has higher scalability and accuracy to identify influential nodes in these snapshot graphs as compared with other comparable algorithms.

30 citations

Journal ArticleDOI
TL;DR: A generalization over the earlier proposed k-nearest leader-based classifier where a novel soft computing approach is used to resolve the uncertainty and combined principles of rough set theory and fuzzy set theory are used to analyze the proposed method.

30 citations

Journal ArticleDOI
01 Sep 2011
TL;DR: Three major components of soft computing viz., genetic algorithm, simulated annealing and differential evolution have been studied for developing fuzzy clustering of microarray gene expression data and support vector machine, a well-known technique for supervised learning, is utilized to improve the result of the clustering techniques.
Abstract: In this article, the performance of three major components of soft computing viz., genetic algorithm, simulated annealing and differential evolution have been studied for developing fuzzy clustering of microarray gene expression data. Microarray technology permits simultaneous monitoring of the expression levels of a large number of genes over different time points. Clustering is an important unsupervised analysis tool in this domain for finding groups of co-expressed genes. In this regard, the performance of the well-known fuzzy c-means algorithm is also studied in addition to the above mentioned soft computing-based approaches. Subsequently, support vector machine, a well-known technique for supervised learning, is utilized to improve the result of the clustering techniques. For this purpose, a fraction of the data points selected from different clusters based on their proximity to the respective centers, is used for training the support vector machine. The cluster assignments of the remaining points are thereafter determined using the trained classifier. Two publicly available benchmark microarray data sets have been used for demonstrating the effectiveness of the proposed approaches. Biological significance tests have been performed to show that the proposed method is able to produce biologically relevant clusters of co-expressed genes.

30 citations

Journal ArticleDOI
01 Mar 2005
TL;DR: A general model of fusion of soft computing and hard computing at the system level as well as at the algorithm level is shown, and several types of tasks, such as scheduling and control are discussed.
Abstract: The design of control systems for large-scale and complex industrial plants involves numerous trade-off problems, such as costs, quality, environmental impact, safety, reliability, accuracy, and robustness. Some of these parameters are even conflicting. Thus, the use of a multidiscipline approach is suggested to satisfy these requirements in an acceptable and well-balanced manner, and a fusion of soft computing and hard computing appears to be a natural and practical choice. Although the state-of-the-art soft computing technology has distinguished features, the use of soft computing technology would be ineffective, and may be doomed to fail, if it is improperly fused with conventional hard computing technology and control processes. Proper fusion is key to success, and a general model of fusion is worth examining. In this paper, through a survey of published literature, a general model of fusion is shown at the system level as well as at the algorithm level. In the system level, soft computing is applied to the upper level in a hierarchical control system, performing human-like tasks, such as forecasting and scheduling, or applied to ill-defined process models for carrying out intelligent control. Hard computing is used at the middle or lower control level for well-defined process models, carrying out coordinate control tasks while maintaining a high level of accurate and safety control. In the case of fusion at the algorithm level, this paper will discuss several types of tasks, such as scheduling and control.

30 citations

Journal ArticleDOI
TL;DR: This paper proposes a hybrid differential evolution – particle swarm optimization (DE-PSO) algorithm to optimize the MC-SVM kernel function and penalty parameters, which improves the classification accuracy of MC- SVM in predicting the health states of a machinery component and consequently increases the accuracy of RUL predictions.
Abstract: Recently, focus on maintenance strategies has been shifted towards prognostic health management (PHM) and a number of state of the art algorithms based on data-driven prognostics have been developed to predict the health states of degrading components based on sensory data. Amongst these algorithms, Multiclass Support Vector Machines (MC-SVM) has gained popularity due to its relatively high classification accuracy, ability to classify multiple patterns and capability to handle noisy / incomplete data. However, its application is limited by the difficulty in determining the required kernel function and penalty parameters. To address this problem, this paper proposes a hybrid differential evolution – particle swarm optimization (DE-PSO) algorithm to optimize the MC-SVM kernel function and penalty parameters. The differential algorithm (DE) obtains the search limit for the SVM parameters, while the particle swarm optimization algorithm (PSO) determines the global optimum parameters for a given training data set. Since degrading machinery components display several degradation stages in their lifetime, the MC-SVM trained with optimum parameters are used to estimate the health states of a degrading machinery component, from which the remaining useful life (RUL) is predicted. This method improves the classification accuracy of MC-SVM in predicting the health states of a machinery component and consequently increases the accuracy of RUL predictions. The feasibility of the method is validated using bearing prognostic run-to-failure data obtained from NASA public data repository. A comparative study between MC-SVM with parameters obtained using simple grid search with n-fold cross validation and MC- SVM with DE-PSO based on prognostic performance metrics reveals that the proposed method has better performance, with all the cases considered falling within a 10 % error margin. The method also outperforms other soft computing methods proposed in literature.

30 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023159
2022270
2021319
2020332
2019313
2018348