Evolutionary Clustering Research Articles

Solving dynamic multi-objective optimization problem of immersed tunnel elements via multi-source evolutionary information clustering method

Dynamic multi-objective optimization problems (DMOPs) are time- and space-varying, thus maintaining/improving the uncertainty degree of evolutionary information (i.e., information entropy) in the population and providing useful knowledge are two important tasks to make dynamic multi-objective evolutionary algorithms (DMOEAs) adapt to changing environments. To achieve the above objectives, a multi-source population clustering (MPC) method is proposed to assist DMOEAs in improving their tracking performance during the full-cycle optimization in the current study. In the MPC, three different information sources are used to provide diverse spatiotemporal evolutionary information, aiding DMOEAs in adapting to various changing environments. Subsequently, an enhanced spectral clustering approach is employed to group all evolutionary individuals from different information sources into many clusters/subspaces. Finally, the selected DMOEA is employed to search all subspaces in parallel via the high-performing computing method. The MPC is incorporated into a regularity model-based multi-objective estimation of distribution algorithm (called as MPC-RM-MEDA) and is compared with six famous DMOEAs on 14 10- and 30-dimensional DMOPs, which are proposed in IEEE Congress on Evolutionary computation 2018. Experimental results demonstrate that the overall tracking performance of the proposed MPC-RM-MEDA is significantly superior to that of other selected competitors in various dynamic environments. Additionally, the MPC-RM-MEDA is utilized to address a real-world DMOP involving an immersed tunnel element. The obtained results and comparison with the knee point-based transfer learning method verify that the MPC is an efficient and dependable approach for enhancing the tracking performance of other DMOEAs in solving actual DMOPs.

A dual typing system establishment and global diversity analysis for sapoviruses

BackgroundThe genus Sapovirus in the family Caliciviridae comprises of a genetically diverse group of viruses that are responsible for causing acute gastroenteritis in both human and animals globally. As the number of sequences continues to grow and more recombinant sequences are identified, the classification criteria of genogroups and genotypes of sapovirus need to be further refined. In this study, we aimed to optimize the classification of sapoviruses.ResultsThrough evolutionary clustering and genetic distance analysis, we have updated the classification criteria for VP1 genogroup and genotypes. We adjusted the original mean values ± 3 standard deviations (SD) of genetic distances to mean values ± 2.5SD, resulting the corresponding cutoff values for the same genotype and genogroup set at <0.161 and <0.503, respectively. Additionally, we established classification criteria for RdRp types and groups, referred to as P-types and P-groups,, with mean values ± 2SD and cutoff values of <0.266 and <0.531 for the same type and group, respectively. This refinement has expanded the VP1 genogroups to thirty-four and identified twenty-four P-groups. For human sapoviruses, the new criteria have resulted in the addition of one genotype, GV.PNA1. Moreover, the new criteria defined three P-groups and 21 P-types for human sapoviruses. Spatial-temporal analysis revealed no specific distribution pattern for human sapoviruses.ConclusionsWe established a dual typing system on classification based on VP1 and RdRp nucleotide sequences for sapoviruses.

Exploring Distribution and Evolution of Pi-ta Haplotypes in Rice Landraces across Different Rice Cultivation Regions in Yunnan.

Background: Rice blast, caused by Magnaporthe oryzae, seriously damages the yield and quality of rice worldwide. Pi-ta is a durable resistance gene that combats M. oryzae carrying AVR-Pita1. However, the distribution of the Pi-ta gene in rice germplasms in Yunnan Province has been inadequately studied. Methods: We analyzed the potential molecular evolution pattern of Pi-ta alleles by examining the diversity in the coding sequence (CDS) among rice varieties. Results: The results revealed that 95% of 405 rice landraces collected from different ecological regions in Yunnan Province carry Pi-ta alleles. We identified 17 nucleotide variation sites in the CDS regions of the Pi-ta gene across 385 rice landraces. These variations led to the identification of 28 Pi-ta haplotypes, encoding 12 novel variants. Among these, 5 Pi-ta haplotypes (62 rice landraces) carried R alleles. The evolutionary cluster and network of the Pi-ta haplotypes suggested that the Pi-ta S alleles were the ancestral alleles, which could potentially evolve into R variants through base substitution. Conclusions: This study suggests that Pi-ta alleles are diverse in the rice landraces in Yunnan, and the Pi-ta sites resistant to blast evolved from the susceptible plants of the rice landraces. These results provide the basis for breeding resistant varieties.

Identification of dynamic networks community by fusing deep learning and evolutionary clustering

Community detection is a critical component of network analysis and a hot topic in social computing. Detecting community structure in dynamic networks has important theoretical and practical implications for understanding the intrinsic function of networks and predicting network behavior. However, the majority of existing dynamic community detection methods adopt shallow models, which have limited ability to excavate complex non-linear structures and tend to generate undesirable community structures. In order to obtain an accurate and robust community structure in dynamic networks, we are inspired by network representation learning and utilize the deep learning to detect evolving communities in dynamic networks. In this paper, we propose a novel dynamic community detection method by fusing Deep Learning and Evolutionary Clustering (DLEC). This work attempts to combine deep learning and evolutionary clustering into a unified framework. First, we propose a matrix construction strategy to fully reveal the inherent community structures via the underlying community memberships. Then, we develop a novel multi-layer deep autoencoder framework that consists of multiple non-linear functions to extract the latent deep representation of the dynamic network. Based on the evolutionary clustering framework, a graph regularization term is introduced to ensure the smoothness of the community evolution. Finally, we employ the K-means clustering algorithm on the low-dimensional network space to obtain the community structure. Extensive experimental results on synthetic and real-world networks show that the proposed DLEC algorithm can effectively detect high-quality communities in dynamic networks.

Can Evolutionary Clustering Have Theoretical Guarantees?

Can Evolutionary Clustering Have Theoretical Guarantees?

Studying word meaning evolution through incremental semantic shift detection

AbstractThe study of semantic shift, that is, of how words change meaning as a consequence of social practices, events and political circumstances, is relevant in Natural Language Processing, Linguistics, and Social Sciences. The increasing availability of large diachronic corpora and advance in computational semantics have accelerated the development of computational approaches to detecting such shift. In this paper, we introduce a novel approach to tracing the evolution of word meaning over time. Our analysis focuses on gradual changes in word semantics and relies on an incremental approach to semantic shift detection (SSD) called What is Done is Done (WiDiD). WiDiD leverages scalable and evolutionary clustering of contextualised word embeddings to detect semantic shift and capture temporal transactions in word meanings. Existing approaches to SSD: (a) significantly simplify the semantic shift problem to cover change between two (or a few) time points, and (b) consider the existing corpora as static. We instead treat SSD as an organic process in which word meanings evolve across tens or even hundreds of time periods as the corpus is progressively made available. This results in an extremely demanding task that entails a multitude of intricate decisions. We demonstrate the applicability of this incremental approach on a diachronic corpus of Italian parliamentary speeches spanning eighteen distinct time periods. We also evaluate its performance on seven popular labelled benchmarks for SSD across multiple languages. Empirical results show that our results are comparable to state-of-the-art approaches, while outperforming the state-of-the-art for certain languages.

From A-to-Z review of clustering validation indices

Data clustering involves identifying latent similarities within a dataset and organizing them into clusters or groups. The outcomes of various clustering algorithms differ as they are susceptible to the intrinsic characteristics of the original dataset, including noise and dimensionality. The effectiveness of such clustering procedures directly impacts the homogeneity of clusters, underscoring the significance of evaluating algorithmic outcomes. Consequently, the assessment of clustering quality presents a significant and complex endeavor. A pivotal aspect affecting clustering validation is the cluster validity metric, which aids in determining the optimal number of clusters. The main goal of this study is to comprehensively review and explain the mathematical operation of internal and external cluster validity indices, but not all, to categorize these indices and to brainstorm suggestions for future advancement of clustering validation research. In addition, we review and evaluate the performance of internal and external clustering validation indices on the most common clustering algorithms, such as the evolutionary clustering algorithm star (ECA*). Finally, we suggest a classification framework for examining the functionality of both internal and external clustering validation measures regarding their ideal values, user-friendliness, responsiveness to input data, and appropriateness across various fields. This classification aids researchers in selecting the appropriate clustering validation measure to suit their specific requirements.

Optimizing architectural multi-dimensional forms; a hybrid approach integrating approximate evolutionary search, clustering and local optimization

Parametric tools in architecture allow for the design of complex and multi-dimensional forms on building facades. Among these, geometric patterns can mitigate direct sunlight and enhance energy efficiency. However, conventional simulation methods and available optimization tools are prohibitively expensive for optimizing such complex forms. To address this challenge, this study proposes two innovative hybrid workflows that integrate parametric modeling, evolutionary approximate and accurate models (NSGA-III), clustering through k-means algorithm, and local search (Tabu search) technique. The outcomes obtained from employing these hybrid approaches demonstrate a substantial reduction in computational time and costs while simultaneously achieving optimal results. Additionally, an extensive comparison between the two proposed methodologies is presented encompassing factors such as performance metrics and computational expenses incurred during implementation. The findings derived from pattern optimization reveal several key insights: increasing pattern counts; dispersing them across the facade; minimizing the distance between the pattern wall from windows; adopting a south-facing orientation with positive vertical rotation – all contribute towards diminishing energy consumption (measured by Energy Use Intensity or EUI) within cold climates. However, material selection for these patterns primarily affects visual comfort levels.

DEDC-IMAE: A deep evolutionary document clustering model with inherited mixed autoencoder

Recently, deep document clustering has been increasingly attracting interest. However, the design of existing deep document clustering methods is limited to processing only static document datasets because applying these methods to dynamic text documents is challenging as the underlying topics of document datasets are not static but evolve. Deep models must learn the cluster-level representations that are inherited. Thus, methods must be explored to implement the inheritance of cluster-level representations. In this study, we overcome the aforementioned challenges and propose a deep evolutionary document clustering model with an inherited mixed autoencoder (DEDC-IMAE), to discover evolutionary document structures with evolutionary topics in dynamic document datasets. An inherited mixed autoencoder (IMAE) is designed to learn an evolutionary semantic representation of each document considering both the inherited historical topic semantics and the document's own characteristics, to reflect the evolved topic semantics. Using the document evolutionary semantics, a deep evolutionary document clustering (DEDC) module is then investigated to learn a clustering partition that reflects the characteristics of document datasets. Experimental results on a real evolutionary text document dataset showed that DEDC-IMAE achieves better evolutionary clustering results than a variety of baseline models.

Social Web in IoT: Can Evolutionary Computation and Clustering Improve Ontology Matching for Social Web of Things?

Social Web in IoT: Can Evolutionary Computation and Clustering Improve Ontology Matching for Social Web of Things?

Multitask Learning for Joint Diagnosis of Multiple Mental Disorders in Resting-State fMRI.

Facing the increasing worldwide prevalence of mental disorders, the symptom-based diagnostic criteria struggle to address the urgent public health concern due to the global shortfall in well-qualified professionals. Thanks to the recent advances in neuroimaging techniques, functional magnetic resonance imaging (fMRI) has surfaced as a new solution to characterize neuropathological biomarkers for detecting functional connectivity (FC) anomalies in mental disorders. However, the existing computer-aided diagnosis models for fMRI analysis suffer from unstable performance on large datasets. To address this issue, we propose an efficient multitask learning (MTL) framework for joint diagnosis of multiple mental disorders using resting-state fMRI data. A novel multiobjective evolutionary clustering algorithm is presented to group regions of interests (ROIs) into different clusters for FC pattern analysis. On the optimal clustering solution, the multicluster multigate mixture-of-expert model is used for the final classification by capturing the highly consistent feature patterns among related diagnostic tasks. Extensive simulation experiments demonstrate that the performance of the proposed framework is superior to that of the other state-of-the-art methods. Moreover, the potential for practical application of the framework is also validated in terms of limited computational resources, real-time analysis, and insufficient training data. The proposed model can identify the remarkable interpretative biomarkers associated with specific mental disorders for clinical interpretation analysis.

Multi-Objective Automatic Clustering Algorithm Based on Evolutionary Multi-Tasking Optimization

Data mining technology is the process of extracting hidden knowledge and potentially useful information from a large number of incomplete, noisy, and random practical application data. The clustering algorithm based on multi-objective evolution has obvious advantages compared with the traditional single-objective method. In order to further improve the performance of evolutionary multi-objective clustering algorithms, this paper proposes a multi-objective automatic clustering model based on evolutionary multi-task optimization. Based on the multi-objective clustering algorithm that automatically determines the value of k, evolutionary multi-task optimization is introduced to deal with multiple clustering tasks simultaneously. A set of non-dominated solutions for clustering results is obtained by concurrently optimizing the overall deviation and connectivity index. Multi-task adjacency coding based on a locus adjacency graph was designed to encode the clustered data. Additionally, an evolutionary operator based on relevance learning was designed to facilitate the evolution of individuals within the population. It also facilitates information transfer between individuals with different tasks, effectively avoiding negative transfer. Finally, the proposed algorithm was applied to both artificial datasets and UCI datasets for testing. It was then compared with traditional clustering algorithms and other multi-objective clustering algorithms. The results verify the advantages of the proposed algorithm in clustering accuracy and algorithm convergence.

An Improved Evolutionary Multi-Objective Clustering Algorithm Based on Autoencoder

Evolutionary multi-objective clustering (EMOC) algorithms have gained popularity recently, as they can obtain a set of clustering solutions in a single run by optimizing multiple objectives. Particularly, in one type of EMOC algorithm, the number of clusters k is taken as one of the multiple objectives to obtain a set of clustering solutions with different k. However, the numbers of clusters k and other objectives are not always in conflict, so it is impossible to obtain the clustering solutions with all different k in a single run. Therefore, evolutionary multi-objective k-clustering (EMO-KC) has recently been proposed to ensure this conflict. However, EMO-KC could not obtain good clustering accuracy on high-dimensional datasets. Moreover, EMO-KC’s validity is not ensured as one of its objectives (SSDexp, which is transformed from the sum of squared distances (SSD)) could not be effectively optimized and it could not avoid invalid solutions in its initialization. In this paper, an improved evolutionary multi-objective clustering algorithm based on autoencoder (AE-IEMOKC) is proposed to improve the accuracy and ensure the validity of EMO-KC. The proposed AE-IEMOKC is established by combining an autoencoder with an improved version of EMO-KC (IEMO-KC) for better accuracy, where IEMO-KC is improved based on EMO-KC by proposing a scaling factor to help effectively optimize the objective of SSDexp and introducing a valid initialization to avoid the invalid solutions. Experimental results on several datasets demonstrate the accuracy and validity of AE-IEMOKC. The results of this paper may provide some useful information for other EMOC algorithms to improve accuracy and convergence.

An adaptive evolutionary multi-objective clustering based on the data properties of the base partitions

Evolutionary multi-objective clustering (EMOC) is a modern clustering technique in which the general concepts of evolutionary multi-objective optimization are applied to the clustering problem. The design and definition of clustering are difficult problems, and the choice of the objective functions and parameter setting of the algorithms are still challenges. In this paper, we propose the adaptive evolutionary multi-objective clustering approach based on data properties (AEMOC). AEMOC considers a new metric to evaluate the base partitions (candidate solutions used in the initial population) generated by minimum spanning tree clustering. This metric is applied to: (1) determine properties of the data, such as separation and overlap; and (2) define an offline selection of objective functions and parameter settings of the multi-objective algorithm. The information regarding the data properties allows AEMOC to avoid unnecessary data processing in datasets in which the initialization provides optimal solutions and optimization is not required. AEMOC presented promising results considering a diverse set of artificial and real-life datasets, based on two aspects: (1) it succeeded in the determination of the data properties of the base partitions and verifying the potential clustering quality, presenting a correlation of 0.8 with a reference metric, and (2) it provided better clustering results than reference EMOC approaches, achieving a general gain in the clustering performance of 3% in real-life datasets and 7% in artificial datasets.

Performance Analysis of Driving Style Identification by using Wolf-Inspired Evolutionary Clustering

Performance Analysis of Driving Style Identification by using Wolf-Inspired Evolutionary Clustering

Load Data Analysis Based on Timestamp-Based Self-Adaptive Evolutionary Clustering

Smart grid system can obtain users' daily load data, and by clustering, we can get users' load profiles to divide them into industrial, commercial and residential types. Load data has the characteristic of changing periodically. Within a period, the load profiles are relatively stable. However, load profiles often changes significantly according to reasons like holidays and season changing. When conducting a continuous clustering task for consecutive days, traditional clustering algorithms cannot consider the time-dimension features into analysis, which may make clustering results be very different even if user behaviors are almost the same. Evolutionary clustering (EC) can be taken into consideration. EC doesn't ignore historical clustering results and makes results more stable in a period. However, when dramatic changes happen in user behaviors, the quality of EC's clustering results will decrease significantly. This paper proposed an optimized evolutionary clustering algorithm: Timestamp-Based Self-Adaptive Evolutionary Clustering (TBSAEC). TBSAEC is based on evolutionary clustering, and takes a heuristic approach to pick the evolutionary parameter to maximize the total quality for one certain timestamp. TBSAEC maintains the stability of continuous-time clustering results while better adapting to changes in user behaviors. Besides, TBSAEC optimize the running efficiency of the algorithm by picking samples in equal portions from historical data instead of the whole data. We applied TBSAEC to the load data of a certain region in east China in 2015, and the results showed that TBSAEC is 3% to 9% higher than the ordinary evolutionary clustering algorithm in total quality, and 87% faster than the ordinary algorithm. Applying TBSAEC to power grid systems can provide more precise users' load profiles, which makes them create more accurate and profitable power plans.

Full genome characterization and evolutionary analysis of Banna virus isolated from Culicoides, mosquitoes and ticks in Yunnan, China.

Banna virus (BAV), a potential pathogen that may cause human encephalitis, is the prototype species of genus Seadornaviru within the family Reoviridae, and has been isolated from a variety of blood-sucking insects and mammals in Asia. Culicoides, Mosquitoes, and Ticks were collected overnight in Yunnan, China, during 2016-2023 using light traps. Virus was isolated from these collected blood-sucking insects and grown using Aedes albopictus (C6/36) cells. Preliminary identification of the virus was performed by agarose gel electrophoresis (AGE). The full genome sequences of the BAVs were determined by full-length amplification of cDNAs (FLAC) and sequenced using next-generation sequencing. In this study, 13 strains BAV were isolated from Culicoides, Mosquitoes and Ticks. Their viral genome consisted of 12 segments of double-stranded RNA (dsRNA), and with three distinct distribution patterns. Sequence analysis showed that Seg-5 of four strains (SJ_M46, SJ_M49, JC_M19-13 and JC_C24-13) has 435 bases nucleotide sequence insertions in their ORF compared to other BAVs, resulting in the length of Seg-5 up to 2128 nt. There are 34 bases sequence deletion in Seg-9 of 3 strains (WS_T06, MS_M166 and MS_M140). Comparison of the coding sequences of VP1, VP2, VP5, VP9 and VP12 of the 13 BAV strains, the results show that VP1, VP2 and VP12 are characterised by high levels of sequence conservation, while VP9 is highly variable, under great pressure to adapt and may be correlated with serotype. While also variable, VP5 appears to be under less adaptive pressure than VP9. Additionally, phylogenetic analysis indicates that the 13 BAV strains locate in the same evolutionary cluster as BAVs isolated from various blood-sucking insects, and are clustered according to geographical distribution. The data obtained herein would be beneficial for the surveillance of evolutionary characteristics of BAV in China and neighboring countries as well as extend the knowledge about its genomic diversity and geographic distribution.

Evolutionary insights and expression dynamics of the CaNFYB transcription factor gene family in pepper (Capsicum annuum) under salinity stress.

Introduction: The Capsicum annuum nuclear factor Y subunit B (CaNFYB) gene family plays a significant role in diverse biological processes, including plant responses to abiotic stressors such as salinity. Methods: In this study, we provide a comprehensive analysis of the CaNFYB gene family in pepper, encompassing their identification, structural details, evolutionary relationships, regulatory elements in promoter regions, and expression profiles under salinity stress. Results and discussion: A total of 19 CaNFYB genes were identified and subsequently characterized based on their secondary protein structures, revealing conserved domains essential for their functionality. Chromosomal distribution showed a non-random localization of these genes, suggesting potential clusters or hotspots for NFYB genes on specific chromosomes. The evolutionary analysis focused on pepper and comparison with other plant species indicated a complex tapestry of relationships with distinct evolutionary events, including gene duplication. Moreover, promoter cis-element analysis highlighted potential regulatory intricacies, with notable occurrences of light-responsive and stress-responsive binding sites. In response to salinity stress, several CaNFYB genes demonstrated significant temporal expression variations, particularly in the roots, elucidating their role in stress adaptation. Particularly CaNFYB01, CaNFYB18, and CaNFYB19, play a pivotal role in early salinity stress response, potentially through specific regulatory mechanisms elucidated by their cis-elements. Their evolutionary clustering with other Solanaceae family members suggests conserved ancestral functions vital for the family's survival under stress. This study provides foundational knowledge on the CaNFYB gene family in C. annuum, paving the way for further research to understand their functional implications in pepper plants and relative species and their potential utilization in breeding programs to enhance salinity tolerance.

Evolutionary clustering in Neotropical biocultural heritage: the Huastec Mayan useful plants

Abstract Biodiversity in the Neotropics includes an extraordinary diversity of plant variation produced by evolution that is useful for human well-being. Traditional knowledge of the Tenek, a Huastec Mayan culture, represents an important biocultural heritage for this realm. Here, we used the information about their useful plants to explore evolutionary biocultural patterns occurring in Neotropics. Our goal was to analyse the phylogenetic distribution of usage guilds, their degree of evolutionary clustering, significant associations, and phylogenetic overlap between guilds to test the hypothesis that Tenek selection of plants is not random but phylogenetically clustered. We found significant phylogenetic clustering in all usage guilds except ceremonial and medicine. Tenek people use a variety of relatively deep plant lineages providing specific services that biocultural processes have promoted in the ecosystems they inhabit. The lineages Asterales, Caryophyllales, Fabales, Lamiales, Malpighiales, and Malvales in eudicots and Poales and Asparagales in monocots concentrated most of the Huastec Mayan useful plants. Multi-functional hot nodes, including Asterales, Fabales, Lamiales, Malvales, Poaceae Sapindales, and Solanales, with phylogenetic overlap between usage guilds, should be major priority targets in conservation planning.

An adaptive dynamic community detection algorithm based on multi-objective evolutionary clustering

PurposeCommunity detection of dynamic networks provides more effective information than static network community detection in the real world. The mainstream method for community detection in dynamic networks is evolutionary clustering, which uses temporal smoothness of community structures to connect snapshots of networks in adjacent time intervals. However, the error accumulation issues limit the effectiveness of evolutionary clustering. While the multi-objective evolutionary approach can solve the issue of fixed settings of the two objective function weight parameters in the evolutionary clustering framework, the traditional multi-objective evolutionary approach lacks self-adaptability.Design/methodology/approachThis paper proposes a community detection algorithm that integrates evolutionary clustering and decomposition-based multi-objective optimization methods. In this approach, a benchmark correction procedure is added to the evolutionary clustering framework to prevent the division results from drifting.FindingsExperimental results demonstrate the superior accuracy of this method compared to similar algorithms in both real and synthetic dynamic datasets.Originality/valueTo enhance the clustering results, adaptive variances and crossover probabilities are designed based on the relative change amounts of the subproblems decomposed by MOEA/D (A Multiobjective Optimization Evolutionary Algorithm based on Decomposition) to dynamically adjust the focus of different evolutionary stages.