scholarly journals Multi-objective Quantum Moth Flame Optimization for Clustering

2020 ◽  
Author(s):  
yassmine Soussi ◽  
Nizar Rokbani ◽  
Ali Wali ◽  
Adel Alimi
Keyword(s):  
Real Life ◽  
Objective Functions ◽  
Cluster Validity ◽  
Number Of Clusters ◽  
Clustering Techniques ◽  
Multi Objective ◽  
Validity Criteria ◽  
Clustering Optimization ◽  
Clustering Problems ◽  
Artificial Datasets

This paper defines a new Moth-Flame optimization version with Quantum behaved moths, QMFO. The multi-objective version of QMFO (MOQMFO) is then applied to solve clustering problems. MOQMFO used three cluster validity criteria as objective functions (the I-index, Con-index and Sym-index) to establish the multi-objective clustering optimization. This paper details the proposal and the preliminary obtained results for clustering real-life datasets (including Iris, Cancer, Newthyroid, Wine, LiverDisorder and Glass) and artificial datasets (including Sph_5_2, Sph_4_3, Sph_6_2, Sph_10_2, Sph_9_2, Pat 1, Pat 2, Long 1, Sizes 5, Spiral, Square 1, Square 4, Twenty and Fourty). Compared with key multi-objectives clustering techniques, the proposal showed interesting results essentially for Iris, Newthyroid, Wine, LiverDisorder, Sph_4_3, Sph_6_2, Long 1, Sizes 5, Twenty and Fourty; and was able to provide the exact number of clusters for all datasets.

scholarly journals Multi-objective Quantum Moth Flame Optimization for Clustering

2020 ◽  
Author(s):  
yassmine Soussi ◽  
Nizar Rokbani ◽  
Ali Wali ◽  
Adel Alimi
Keyword(s):  
Real Life ◽  
Objective Functions ◽  
Cluster Validity ◽  
Number Of Clusters ◽  
Clustering Techniques ◽  
Multi Objective ◽  
Validity Criteria ◽  
Clustering Optimization ◽  
Clustering Problems ◽  
Artificial Datasets

This paper defines a new Moth-Flame optimization version with Quantum behaved moths, QMFO. The multi-objective version of QMFO (MOQMFO) is then applied to solve clustering problems. MOQMFO used three cluster validity criteria as objective functions (the I-index, Con-index and Sym-index) to establish the multi-objective clustering optimization. This paper details the proposal and the preliminary obtained results for clustering real-life datasets (including Iris, Cancer, Newthyroid, Wine, LiverDisorder and Glass) and artificial datasets (including Sph_5_2, Sph_4_3, Sph_6_2, Sph_10_2, Sph_9_2, Pat 1, Pat 2, Long 1, Sizes 5, Spiral, Square 1, Square 4, Twenty and Fourty). Compared with key multi-objectives clustering techniques, the proposal showed interesting results essentially for Iris, Newthyroid, Wine, LiverDisorder, Sph_4_3, Sph_6_2, Long 1, Sizes 5, Twenty and Fourty; and was able to provide the exact number of clusters for all datasets.

scholarly journals Multi-objective PSO with Pareto Neighborhood topology for Clustering

2021 ◽  
Author(s):  
Yassmine Soussi ◽  
Nizar Rokbani ◽  
Ali Wali ◽  
Adel Alimi
Keyword(s):  
Real Life ◽  
Objective Functions ◽  
Validity Index ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Silhouette Index ◽  
A New Technique ◽  
Clustering Optimization ◽  
Neighborhood Topology ◽  
Pareto Optimal Fronts

In this paper a new technique is integrated to Multi-Objective Particle Swarm Optimization (MOPSO) algorithm, named Pareto Neighborhood (PN) topology, to produce MOPSO-PN algorithm. This technique involves iteratively selecting a set of best solutions from the Pareto-Optimal-Fronts and trying to explore them in order to find better clustering results in the next iteration. MOPSO-PN was then used as a Multi?Objective Clustering Optimization (MOCO) Algorithm, it was tested on various datasets (real-life and artificial datasets). Two scenarios have been used to test the performances of MOPSO-PN for clustering: In the first scenario MOPSO-PN utilizes, as objective functions, two clusters validity index (Silhouette?Index and overall-cluster-deviation), three datasets for test, four algorithms for comparison and the average Minkowski Score as metric for evaluating the final clustering result; In the second scenario MOPSO-PN used, as objectives functions, three clusters validity index (I-index, Con-index and Sym?index), 20 datasets for test, ten algorithms for comparison and the F-Measure as metric for evaluating the final clustering result. In both scenarios, MOPSO-PN provided a competitive clustering results and a correct number of clusters for all datasets.

scholarly journals Multi-objective PSO with Pareto Neighborhood topology for Clustering

2021 ◽  
Author(s):  
Yassmine Soussi ◽  
Nizar Rokbani ◽  
Seyedali Mirjalili ◽  
Ali Wali ◽  
Adel Alimi
Keyword(s):  
Real Life ◽  
Objective Functions ◽  
Validity Index ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Silhouette Index ◽  
A New Technique ◽  
Clustering Optimization ◽  
Neighborhood Topology ◽  
Pareto Optimal Fronts

In this paper a new technique is integrated to Multi-Objective Particle Swarm Optimization (MOPSO) algorithm, named Pareto Neighborhood (PN) topology, to produce MOPSO-PN algorithm. This technique involves iteratively selecting a set of best solutions from the Pareto-Optimal-Fronts and trying to explore them in order to find better clustering results in the next iteration. MOPSO-PN was then used as a Multi?Objective Clustering Optimization (MOCO) Algorithm, it was tested on various datasets (real-life and artificial datasets). Two scenarios have been used to test the performances of MOPSO-PN for clustering: In the first scenario MOPSO-PN utilizes, as objective functions, two clusters validity index (Silhouette?Index and overall-cluster-deviation), three datasets for test, four algorithms for comparison and the average Minkowski Score as metric for evaluating the final clustering result; In the second scenario MOPSO-PN used, as objectives functions, three clusters validity index (I-index, Con-index and Sym?index), 20 datasets for test, ten algorithms for comparison and the F-Measure as metric for evaluating the final clustering result. In both scenarios, MOPSO-PN provided a competitive clustering results and a correct number of clusters for all datasets.

scholarly journals Multi-objective PSO with Pareto Neighborhood topology for Clustering

2021 ◽  
Author(s):  
Yassmine Soussi ◽  
Nizar Rokbani ◽  
Ali Wali ◽  
Adel Alimi
Keyword(s):  
Real Life ◽  
Objective Functions ◽  
Validity Index ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Silhouette Index ◽  
A New Technique ◽  
Clustering Optimization ◽  
Neighborhood Topology ◽  
Pareto Optimal Fronts

In this paper a new technique is integrated to Multi-Objective Particle Swarm Optimization (MOPSO) algorithm, named Pareto Neighborhood (PN) topology, to produce MOPSO-PN algorithm. This technique involves iteratively selecting a set of best solutions from the Pareto-Optimal-Fronts and trying to explore them in order to find better clustering results in the next iteration. MOPSO-PN was then used as a Multi?Objective Clustering Optimization (MOCO) Algorithm, it was tested on various datasets (real-life and artificial datasets). Two scenarios have been used to test the performances of MOPSO-PN for clustering: In the first scenario MOPSO-PN utilizes, as objective functions, two clusters validity index (Silhouette?Index and overall-cluster-deviation), three datasets for test, four algorithms for comparison and the average Minkowski Score as metric for evaluating the final clustering result; In the second scenario MOPSO-PN used, as objectives functions, three clusters validity index (I-index, Con-index and Sym?index), 20 datasets for test, ten algorithms for comparison and the F-Measure as metric for evaluating the final clustering result. In both scenarios, MOPSO-PN provided a competitive clustering results and a correct number of clusters for all datasets.

scholarly journals Bio-inspired multiobjective clustering optimization: A survey and a proposal

2017 ◽  
Vol 6 (2) ◽  
pp. 10 ◽  
Author(s):  
Danilo Cunha ◽  
Dávila Cruz ◽  
Alexandre Politi ◽  
Leandro Nunes de Castro ◽  
Renato Dourado Maia
Keyword(s):  
Objective Functions ◽  
Cluster Validity ◽  
High Quality ◽  
Cluster Validity Indices ◽  
Validity Indices ◽  
Clustering Quality ◽  
Clustering Optimization ◽  
The Many ◽  
Clustering Problems

Multiobjective clustering techniques have been used to simultaneously consider several complementary aspects of clustering quality. They optimize two or more cluster validity indices simultaneously, they lead to high-quality results, and have emerged as attractive and robust alternatives for solving clustering problems. This paper provides a brief review of bio-Inspired multiobjective clustering, and proposes a bee-inspired multiobjective optimization (MOO) algorithm, named cOptBees-MO, to solve multiobjective data clustering problems. In its survey part, a brief tutorial on MOO and multiobjective clustering optimization (MOCO) is presented, followed by a review of the main works in the area. Particular attention is given to the many objective functions used in MOCO. To evaluate the performance of the algorithm it was executed for various datasets and the results presented high quality clusters, diverse solutions an the automatic determination of a suitable number of clusters.

scholarly journals Towards Expert-Inspired Automatic Criterion to Cut a Dendrogram for Real-Industrial Applications

2021 ◽  
Author(s):  
Shikha Suman ◽  
Ashutosh Karna ◽  
Karina Gibert
Keyword(s):  
Hierarchical Clustering ◽  
Clustering Algorithms ◽  
Computational Cost ◽  
Real Life ◽  
Ground Truth ◽  
Industrial Applications ◽  
Underlying Structure ◽  
Cluster Validity ◽  
Cluster Validity Index ◽  
Number Of Clusters

Hierarchical clustering is one of the most preferred choices to understand the underlying structure of a dataset and defining typologies, with multiple applications in real life. Among the existing clustering algorithms, the hierarchical family is one of the most popular, as it permits to understand the inner structure of the dataset and find the number of clusters as an output, unlike popular methods, like k-means. One can adjust the granularity of final clustering to the goals of the analysis themselves. The number of clusters in a hierarchical method relies on the analysis of the resulting dendrogram itself. Experts have criteria to visually inspect the dendrogram and determine the number of clusters. Finding automatic criteria to imitate experts in this task is still an open problem. But, dependence on the expert to cut the tree represents a limitation in real applications like the fields industry 4.0 and additive manufacturing. This paper analyses several cluster validity indexes in the context of determining the suitable number of clusters in hierarchical clustering. A new Cluster Validity Index (CVI) is proposed such that it properly catches the implicit criteria used by experts when analyzing dendrograms. The proposal has been applied on a range of datasets and validated against experts ground-truth overcoming the results obtained by the State of the Art and also significantly reduces the computational cost.

Two-Stage Clustering Based on Cluster Validity Measures

2018 ◽  
Vol 22 (1) ◽  
pp. 54-61
Author(s):  
Yukihiro Hamasuna ◽  
Ryo Ozaki ◽  
Yasunori Endo ◽  
◽  
◽  
...  
Keyword(s):  
Large Scale ◽  
Cluster Validity ◽  
Clustering Method ◽  
Significant Cluster ◽  
Two Stage ◽  
Number Of Clusters ◽  
Object A ◽  
Second Stage ◽  
Validity Measures ◽  
Artificial Datasets

To handle a large-scale object, a two-stage clustering method has been previously proposed. The method generates a large number of clusters during the first stage and merges clusters during the second stage. In this paper, a novel two-stage clustering method is proposed by introducing cluster validity measures as the merging criterion during the second stage. The significant cluster validity measures used to evaluate cluster partitions and determine the suitable number of clusters act as the criteria for merging clusters. The performance of the proposed method based on six typical indices is compared with eight artificial datasets. These experiments show that a trace of the fuzzy covariance matrixWtrand its kernelizationKWtrare quite effective when applying the proposed method, and obtain better results than the other indices.

scholarly journals A comparative study of many-objective optimizers on large-scale many-objective software clustering problems

2021 ◽  
Author(s):  
Amarjeet Prajapati
Keyword(s):  
Large Scale ◽  
Optimization Problems ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Software Clustering ◽  
Multi Objective ◽  
The Past ◽  
Decision Variables ◽  
Clustering Problems ◽  
Special Case

AbstractOver the past 2 decades, several multi-objective optimizers (MOOs) have been proposed to address the different aspects of multi-objective optimization problems (MOPs). Unfortunately, it has been observed that many of MOOs experiences performance degradation when applied over MOPs having a large number of decision variables and objective functions. Specially, the performance of MOOs rapidly decreases when the number of decision variables and objective functions increases by more than a hundred and three, respectively. To address the challenges caused by such special case of MOPs, some large-scale multi-objective optimization optimizers (L-MuOOs) and large-scale many-objective optimization optimizers (L-MaOOs) have been developed in the literature. Even after vast development in the direction of L-MuOOs and L-MaOOs, the supremacy of these optimizers has not been tested on real-world optimization problems containing a large number of decision variables and objectives such as large-scale many-objective software clustering problems (L-MaSCPs). In this study, the performance of nine L-MuOOs and L-MaOOs (i.e., S3-CMA-ES, LMOSCO, LSMOF, LMEA, IDMOPSO, ADC-MaOO, NSGA-III, H-RVEA, and DREA) is evaluated and compared over five L-MaSCPs in terms of IGD, Hypervolume, and MQ metrics. The experimentation results show that the S3-CMA-ES and LMOSCO perform better compared to the LSMOF, LMEA, IDMOPSO, ADC-MaOO, NSGA-III, H-RVEA, and DREA in most of the cases. The LSMOF, LMEA, IDMOPSO, ADC-MaOO, NSGA-III, and DREA, are the average performer, and H-RVEA is the worst performer.

scholarly journals Multi-objective formulation of MSA for phylogeny estimation

2018 ◽  
Author(s):  
Muhammad Ali Nayeem ◽  
Md. Shamsuzzoha Bayzid ◽  
Atif Hasan Rahman ◽  
Rifat Shahriyar ◽  
M. Sohel Rahman
Keyword(s):  
Phylogenetic Tree ◽  
Phylogenetic Trees ◽  
Real Life ◽  
Objective Functions ◽  
Multiple Sequence ◽  
Basic Step ◽  
Multi Objective ◽  
Phylogeny Estimation ◽  
And Function ◽  
The Impact

AbstractMultiple sequence alignment (MSA) is a basic step in many analyses in computational biology, including predicting the structure and function of proteins, orthology prediction and estimating phylogenies. The objective of MSA is to infer the homology among the sequences of chosen species. Commonly, the MSAs are inferred by optimizing a single function or objective. The alignments estimated under one criterion may be different to the alignments generated by other criteria, inferring discordant homologies and thus leading to different evolutionary histories relating the sequences. In recent past, researchers have advocated for the multi-objective formulation of MSA, to address this issue, where multiple conflicting objective functions are being optimized simultaneously to generate a set of alignments. However, no theoretical or empirical justification with respect to a real-life application has been shown for a particular multi-objective formulation. In this study, we investigate the impact of multi-objective formulation in the context of phylogenetic tree estimation. Employing multi-objective metaheuristics, we demonstrate that trees estimated on the alignments generated by multi-objective formulation are substantially better than the trees estimated by the state-of-the-art MSA tools, including PASTA, MUSCLE, CLUSTAL, MAFFT etc. We also demonstrate that highly accurate alignments with respect to popular measures like sum-of-pair (SP) score and total-column (TC) score do not necessarily lead to highly accurate phylogenetic trees. Thus in essence we ask the question whether a phylogeny-aware metric can guide us in choosing appropriate multi-objective formulations that can result in better phylogeny estimation. And we answer the question affirmatively through carefully designed extensive empirical study. As a by-product we also suggest a methodology for primary selection of a set of objective functions for a multi-objective formulation based on the association with the resulting phylogenetic tree.

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