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Tawhid MA, Ibrahim AM. An efficient hybrid swarm intelligence optimization algorithm for solving nonlinear systems and clustering problems. Soft comput 2023. [DOI: 10.1007/s00500-022-07780-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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2
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Zhang Y, Sun B, Li Y, Zhao S, Zhu X, Ma W, Ma F, Wu L. Research on the Physics-Intelligence Hybrid Theory Based Dynamic Scenario Library Generation for Automated Vehicles. SENSORS (BASEL, SWITZERLAND) 2022; 22:8391. [PMID: 36366091 PMCID: PMC9656793 DOI: 10.3390/s22218391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/11/2022] [Revised: 10/23/2022] [Accepted: 10/27/2022] [Indexed: 06/16/2023]
Abstract
The testing and evaluation system has been the key technology and security with its necessity in the development and deployment of maturing automated vehicles. In this research, the physics-intelligence hybrid theory-based dynamic scenario library generation method is proposed to improve system performance, in particular, the testing efficiency and accuracy for automated vehicles. A general framework of the dynamic scenario library generation is established. Then, the parameterized scenario based on the dimension optimization method is specified to obtain the effective scenario element set. Long-tail functions for performance testing of specific ODD are constructed as optimization boundaries and critical scenario searching methods are proposed based on the node optimization and sample expansion methods for the low-dimensional scenario library generation and the reinforcement learning for the high-dimensional one, respectively. The scenario library generation method is evaluated with the naturalistic driving data (NDD) of the intelligent electric vehicle in the field test. Results show better efficient and accuracy performances compared with the ideal testing library and the NDD, respectively, in both low- and high-dimensional scenarios.
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Affiliation(s)
- Yufei Zhang
- State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130025, China
| | - Bohua Sun
- State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130025, China
| | - Yaxin Li
- State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130025, China
| | - Shuai Zhao
- China Automotive Technology & Research Center (CATARC) Co., Ltd., Tianjin 300399, China
- College of Intelligence and Computing, Tianjin University, Tianjin 300072, China
| | - Xianglei Zhu
- China Automotive Technology & Research Center (CATARC) Co., Ltd., Tianjin 300399, China
- College of Intelligence and Computing, Tianjin University, Tianjin 300072, China
| | - Wenxiao Ma
- State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130025, China
| | - Fangwu Ma
- State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130025, China
| | - Liang Wu
- State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130025, China
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Magnetic data interpretation for 2D dikes by the metaheuristic bat algorithm: sustainable development cases. Sci Rep 2022; 12:14206. [PMID: 35987962 PMCID: PMC9392764 DOI: 10.1038/s41598-022-18334-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Accepted: 08/09/2022] [Indexed: 12/01/2022] Open
Abstract
Metaheuristic algorithms are increasingly being utilized as a global optimal method in the inversion and modeling of magnetic data. We proposed the Bat Algorithm Optimization (BAO) technique that is based on bat echolocation performance to find the global optimum solution. The best-estimated source parameters that correspond to the objective function minimum value are obtained after achieving the global optimum (best) solution. The suggested BAO technique does not require any prior knowledge; rather, it is a global search method that provides an effective tool for scanning the space of data to appraise sources parameters. The BAO technique is applied to magnetic data in the class of dipping and vertical dikes along 2D profiles to estimate the dimensional source parameters that include the depth to top, origin location, amplitude coefficient, index angle of magnetization, and width of the dipping dikes. The BAO technique has been used for single and multiple dikes structures. The accuracy and stability of the BAO technique are achieved on different synthetic examples of free and noisy data for single and multiple cases. Furthermore, the presented BAO technique was effectively utilized in three field examples from China and Egypt for iron ore deposits and metavolcanics basalt rock investigations. Overall, the BAO technique recovered inversion outcomes are in good agreement with borehole, geology, and published literature results.
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Kumar N, Kumar H. A fuzzy clustering technique for enhancing the convergence performance by using improved Fuzzy c-means and Particle Swarm Optimization algorithms. DATA KNOWL ENG 2022. [DOI: 10.1016/j.datak.2022.102050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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5
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Hussain SF, Butt IA, Hanif M, Anwar S. Clustering uncertain graphs using ant colony optimization (ACO). Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07063-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Singh H, Kumar Y. An Enhanced Version of Cat Swarm Optimization Algorithm for Cluster Analysis. INTERNATIONAL JOURNAL OF APPLIED METAHEURISTIC COMPUTING 2022. [DOI: 10.4018/ijamc.2022010108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Clustering is an unsupervised machine learning technique that optimally organizes the data objects in a group of clusters. In present work, a meta-heuristic algorithm based on cat intelligence is adopted for optimizing clustering problems. Further, to make the cat swarm algorithm (CSO) more robust for partitional clustering, some modifications are incorporated in it. These modifications include an improved solution search equation for balancing global and local searches, accelerated velocity equation for addressing diversity, especially in tracing mode. Furthermore, a neighborhood-based search strategy is introduced to handle the local optima and premature convergence problems. The performance of enhanced cat swarm optimization (ECSO) algorithm is tested on eight real-life datasets and compared with the well-known clustering algorithms. The simulation results confirm that the proposed algorithm attains the optimal results than other clustering algorithms.
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Affiliation(s)
| | - Yugal Kumar
- Jaypee University of Infromation Technoogy, India
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Mohanty PP, Nayak SK. A Modified Cuckoo Search Algorithm for Data Clustering. INTERNATIONAL JOURNAL OF APPLIED METAHEURISTIC COMPUTING 2022. [DOI: 10.4018/ijamc.2022010101] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Clustering of data is one of the necessary data mining techniques, where similar objects are grouped in the same cluster. In recent years, many nature-inspired based clustering techniques have been proposed, which have led to some encouraging results. This paper proposes a Modified Cuckoo Search (MoCS) algorithm. In this proposed work, an attempt has been made to balance the exploration of the Cuckoo Search (CS) algorithm and to increase the potential of the exploration to avoid premature convergence. This algorithm is tested using fifteen benchmark test functions and is proved as an efficient algorithm in comparison to the CS algorithm. Further, this method is compared with well-known nature-inspired algorithms such as Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), Particle Swarm Optimization with Age Group topology (PSOAG) and CS algorithm for clustering of data using six real datasets. The experimental results indicate that the MoCS algorithm achieves better results as compared to other algorithms in finding optimal cluster centers.
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Affiliation(s)
- Preeti Pragyan Mohanty
- Institute of Technical Education and Research, Siksha 'O' Anusandhan University, Bhubaneswar, India
| | - Subrat Kumar Nayak
- Institute of Technical Education and Research, Siksha 'O' Anusandhan University, Bhubaneswar, India
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Sawalmeh A, Othman NS, Liu G, Khreishah A, Alenezi A, Alanazi A. Power-Efficient Wireless Coverage Using Minimum Number of UAVs. SENSORS 2021; 22:s22010223. [PMID: 35009766 PMCID: PMC8749821 DOI: 10.3390/s22010223] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Revised: 12/17/2021] [Accepted: 12/23/2021] [Indexed: 11/16/2022]
Abstract
Unmanned aerial vehicles (UAVs) can be deployed as backup aerial base stations due to cellular outage either during or post natural disaster. In this paper, an approach involving multi-UAV three-dimensional (3D) deployment with power-efficient planning was proposed with the objective of minimizing the number of UAVs used to provide wireless coverage to all outdoor and indoor users that minimizes the required UAV transmit power and satisfies users’ required data rate. More specifically, the proposed algorithm iteratively invoked a clustering algorithm and an efficient UAV 3D placement algorithm, which aimed for maximum wireless coverage using the minimum number of UAVs while minimizing the required UAV transmit power. Two scenarios where users are uniformly and non-uniformly distributed were considered. The proposed algorithm that employed a Particle Swarm Optimization (PSO)-based clustering algorithm resulted in a lower number of UAVs needed to serve all users compared with that when a K-means clustering algorithm was employed. Furthermore, the proposed algorithm that iteratively invoked a PSO-based clustering algorithm and PSO-based efficient UAV 3D placement algorithms reduced the execution time by a factor of ≈1/17 and ≈1/79, respectively, compared to that when the Genetic Algorithm (GA)-based and Artificial Bees Colony (ABC)-based efficient UAV 3D placement algorithms were employed. For the uniform distribution scenario, it was observed that the proposed algorithm required six UAVs to ensure 100% user coverage, whilst the benchmarker algorithm that utilized Circle Packing Theory (CPT) required five UAVs but at the expense of 67% of coverage density.
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Affiliation(s)
- Ahmad Sawalmeh
- Computer Science Department, Northern Border University, Arar 91431, Saudi Arabia
- Remote Sensing Unit, Northern Border University, Arar 91431, Saudi Arabia;
- Correspondence: or
| | - Noor Shamsiah Othman
- Department of Electrical and Electronics Engineering, Universiti Tenaga Nasional, Kajang 43000, Selangor, Malaysia;
| | - Guanxiong Liu
- Department of Electrical and Computer Engineering, Newark College of Engineering, New Jersey Institute of Technology, University Heights, Newark, NJ 07102, USA; (G.L.); (A.K.)
| | - Abdallah Khreishah
- Department of Electrical and Computer Engineering, Newark College of Engineering, New Jersey Institute of Technology, University Heights, Newark, NJ 07102, USA; (G.L.); (A.K.)
| | - Ali Alenezi
- Remote Sensing Unit, Northern Border University, Arar 91431, Saudi Arabia;
- Electrical Engineering Department, Northern Border University, Arar 91431, Saudi Arabia;
| | - Abdulaziz Alanazi
- Electrical Engineering Department, Northern Border University, Arar 91431, Saudi Arabia;
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Asadi-Zonouz M, Amin-Naseri MR, Ardjmand E. A modified unconscious search algorithm for data clustering. EVOLUTIONARY INTELLIGENCE 2021. [DOI: 10.1007/s12065-021-00578-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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11
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Kaur A, Kumar Y. A new metaheuristic algorithm based on water wave optimization for data clustering. EVOLUTIONARY INTELLIGENCE 2021. [DOI: 10.1007/s12065-020-00562-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Pariserum Perumal S, Sannasi G, Arputharaj K. FIRMACA-Fuzzy intelligent recommendation model using ant clustering algorithm for social networking. SN APPLIED SCIENCES 2020. [DOI: 10.1007/s42452-020-03486-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
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14
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Quantum-inspired ant lion optimized hybrid k-means for cluster analysis and intrusion detection. Knowl Based Syst 2020. [DOI: 10.1016/j.knosys.2020.106167] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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Abasi AK, Khader AT, Al-Betar MA, Naim S, Alyasseri ZAA, Makhadmeh SN. A novel hybrid multi-verse optimizer with K-means for text documents clustering. Neural Comput Appl 2020. [DOI: 10.1007/s00521-020-04945-0] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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17
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Aljarah I, Mafarja M, Heidari AA, Faris H, Mirjalili S. Multi-verse Optimizer: Theory, Literature Review, and Application in Data Clustering. NATURE-INSPIRED OPTIMIZERS 2020. [DOI: 10.1007/978-3-030-12127-3_8] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
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18
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Qasim T, Bhatti N. A hybrid swarm intelligence based approach for abnormal event detection in crowded environments. Pattern Recognit Lett 2019. [DOI: 10.1016/j.patrec.2019.09.003] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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19
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Dhal KG, Gálvez J, Das S. Toward the modification of flower pollination algorithm in clustering-based image segmentation. Neural Comput Appl 2019. [DOI: 10.1007/s00521-019-04585-z] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
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20
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Xie H, Zhang L, Lim CP, Yu Y, Liu C, Liu H, Walters J. Improving K-means clustering with enhanced Firefly Algorithms. Appl Soft Comput 2019. [DOI: 10.1016/j.asoc.2019.105763] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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21
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22
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Dhal KG, Das A, Ray S, Das S. A Clustering Based Classification Approach Based on Modified Cuckoo Search Algorithm. PATTERN RECOGNITION AND IMAGE ANALYSIS 2019. [DOI: 10.1134/s1054661819030052] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
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23
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Kushwaha N, Pant M. Fuzzy electromagnetic optimisation clustering algorithm for collaborative filtering. J EXP THEOR ARTIF IN 2019. [DOI: 10.1080/0952813x.2019.1647557] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Affiliation(s)
- Neetu Kushwaha
- Department of ASE, Indian Institute of Technology Roorkee, Roorkee, India
| | - Millie Pant
- Department of ASE, Indian Institute of Technology Roorkee, Roorkee, India
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24
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Salehnia N, Salehnia N, Ansari H, Kolsoumi S, Bannayan M. Climate data clustering effects on arid and semi-arid rainfed wheat yield: a comparison of artificial intelligence and K-means approaches. INTERNATIONAL JOURNAL OF BIOMETEOROLOGY 2019; 63:861-872. [PMID: 31115656 DOI: 10.1007/s00484-019-01699-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2018] [Revised: 02/05/2019] [Accepted: 02/16/2019] [Indexed: 06/09/2023]
Abstract
Clustering algorithms are critical data mining techniques used to analyze a wide range of data. This study compares the utility of ant colony optimization (ACO), genetic algorithm (GA), and K-means methods to cluster climatic variables affecting the yield of rainfed wheat in northeast Iran from 1984 to 2010 (27 years). These variables included sunshine hours, wind speed, relative humidity, precipitation, maximum temperature, minimum temperature, and the number of wet days. Seven climatic factors with higher correlations with detrended rainfed wheat yield were selected based on Pearson correlation coefficient significance (P value < 0.1). Three variables (i.e., sunshine hours, wind, and average relative humidity) were excluded for clustering. In the next step based on Pearson correlation (P value < 0.05) between the yield, and the seven climate attributes, fitness function, and silhouette index, only four attributes with higher correlation in its cluster were selected for reclustering. Four climate attributes had an extensive association with yield, so we used four-dimensional clustering to describe the common characteristics of low-, medium-, and high-yielding years, and this is the significance of this research that we have done four-dimensional clustering. The silhouette index showed that the best number of clusters for each station was equal to three clusters. At the last step, reclustering was done through the best-selected method. The results yielded that GA was the best method.
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Affiliation(s)
- Nasrin Salehnia
- Faculty of Agriculture, Ferdowsi University of Mashhad, P.O. Box 91775-1163, Mashhad, Iran
- AgriMetSoft, Roshd Center, Ferdowsi University of Mashhad, P.O. Box 9177-949207, Mashhad, Iran
| | - Narges Salehnia
- Department of Economics, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Hossein Ansari
- Faculty of Agriculture, Ferdowsi University of Mashhad, P.O. Box 91775-1163, Mashhad, Iran.
| | - Sohrab Kolsoumi
- AgriMetSoft, Roshd Center, Ferdowsi University of Mashhad, P.O. Box 9177-949207, Mashhad, Iran
| | - Mohammad Bannayan
- Faculty of Agriculture, Ferdowsi University of Mashhad, P.O. Box 91775-1163, Mashhad, Iran
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Singh H, Kumar Y. Cellular Automata Based Model for E-Healthcare Data Analysis. INTERNATIONAL JOURNAL OF INFORMATION SYSTEM MODELING AND DESIGN 2019. [DOI: 10.4018/ijismd.2019070101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
E-healthcare is warm area of research and a number of algorithms have been applied to classify healthcare data. In the healthcare field, a large amount of clinical data is generated through MRI, CT scans, and other diagnostic tools. Healthcare analytics are used to analyze the clinical data of patient records, disease diagnosis, cost, hospital management, etc. Analytical techniques and data visualization are used to get the real time information. Further, this information can be used for decision making. Also, this information is useful for the better treatment of patients. In this work, an improved big bang-big crunch (BB-BC) based clustering algorithm is applied to analyze healthcare data. Cluster analysis is an important task in the field of data analysis and can be used to understand the organization of data. In this work, two healthcare datasets, CMC and cancer, are used and the proposed algorithm obtains better results when compared to MEBB-BC, BB-BC, GA, PSO and K-means algorithms. The performance of the improved BB-BC algorithm is also examined against benchmark clustering datasets. The simulation results showed that proposed algorithm improves the clustering results significantly when compared to other algorithms.
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Affiliation(s)
- Hakam Singh
- Department of Computer Science and Engineering, Jaypee University of Information Technology Waknaghat, Solan, Himachal Pradesh, India
| | - Yugal Kumar
- Department of Computer Science and Engineering, Jaypee University of Information Technology Waknaghat, Solan, Himachal Pradesh, India
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26
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Senthilnath J, Kulkarni S, Suresh S, Yang XS, Benediktsson JA. FPA clust: evaluation of the flower pollination algorithm for data clustering. EVOLUTIONARY INTELLIGENCE 2019. [DOI: 10.1007/s12065-019-00254-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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27
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Fahy C, Yang S, Gongora M. Ant Colony Stream Clustering: A Fast Density Clustering Algorithm for Dynamic Data Streams. IEEE TRANSACTIONS ON CYBERNETICS 2019; 49:2215-2228. [PMID: 29993761 DOI: 10.1109/tcyb.2018.2822552] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
A data stream is a continuously arriving sequence of data and clustering data streams requires additional considerations to traditional clustering. A stream is potentially unbounded, data points arrive online and each data point can be examined only once. This imposes limitations on available memory and processing time. Furthermore, streams can be noisy and the number of clusters in the data and their statistical properties can change over time. This paper presents an online, bio-inspired approach to clustering dynamic data streams. The proposed ant colony stream clustering (ACSC) algorithm is a density-based clustering algorithm, whereby clusters are identified as high-density areas of the feature space separated by low-density areas. ACSC identifies clusters as groups of micro-clusters. The tumbling window model is used to read a stream and rough clusters are incrementally formed during a single pass of a window. A stochastic method is employed to find these rough clusters, this is shown to significantly speeding up the algorithm with only a minor cost to performance, as compared to a deterministic approach. The rough clusters are then refined using a method inspired by the observed sorting behavior of ants. Ants pick-up and drop items based on the similarity with the surrounding items. Artificial ants sort clusters by probabilistically picking and dropping micro-clusters based on local density and local similarity. Clusters are summarized using their constituent micro-clusters and these summary statistics are stored offline. Experimental results show that the clustering quality of ACSC is scalable, robust to noise and favorable to leading ant clustering and stream-clustering algorithms. It also requires fewer parameters and less computational time.
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29
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A review on the self and dual interactions between machine learning and optimisation. PROGRESS IN ARTIFICIAL INTELLIGENCE 2019. [DOI: 10.1007/s13748-019-00185-z] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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30
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Dinkar SK, Deep K. Opposition-based antlion optimizer using Cauchy distribution and its application to data clustering problem. Neural Comput Appl 2019. [DOI: 10.1007/s00521-019-04174-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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31
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Clustering analysis using a novel locality-informed grey wolf-inspired clustering approach. Knowl Inf Syst 2019. [DOI: 10.1007/s10115-019-01358-x] [Citation(s) in RCA: 48] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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32
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Danish Z, Shah H, Tairan N, Gazali R, Badshah A. Global Artificial Bee Colony Search Algorithm for Data Clustering. INTERNATIONAL JOURNAL OF SWARM INTELLIGENCE RESEARCH 2019. [DOI: 10.4018/ijsir.2019040104] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Data clustering is a widespread data compression, vector quantization, data analysis, and data mining technique. In this work, a modified form of ABC, i.e. global artificial bee colony search algorithm (GABCS) is applied to data clustering. In GABCS the modification is due to the fact that experienced bees can use past information of quantity of food and position to adjust their movements in a search space. Due to this fact, solution search equations of the canonical ABC are modified in GABCS and applied to three famous real datasets in this work i.e. iris, thyroid, wine, accessed from the UCI database for the purpose of data clustering and results were compared with few other stated algorithms such as K-NM-PSO, TS, ACO, GA, SA and ABC. The results show that while calculating intra-clustering distances and computation time on all three real datasets, the proposed GABCS algorithm gives far better performance than other algorithms whereas calculating computation numbers it performs adequately as compared to typical ABC.
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Affiliation(s)
| | - Habib Shah
- King Khalid University, Abha, Saudi Arabia
| | | | | | - Akhtar Badshah
- Department of Software Engineering, University of Malakand, Pakistan
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33
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A new meta-heuristic algorithm based on chemical reactions for partitional clustering problems. EVOLUTIONARY INTELLIGENCE 2019. [DOI: 10.1007/s12065-019-00221-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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34
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35
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Queiroga E, Subramanian A, dos Anjos F. Cabral L. Continuous greedy randomized adaptive search procedure for data clustering. Appl Soft Comput 2018. [DOI: 10.1016/j.asoc.2018.07.031] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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36
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Gao C, Liang M, Li X, Zhang Z, Wang Z, Zhou Z. Network Community Detection Based on the Physarum-Inspired Computational Framework. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2018; 15:1916-1928. [PMID: 27992347 DOI: 10.1109/tcbb.2016.2638824] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Community detection is a crucial and essential problem in the structure analytics of complex networks, which can help us understand and predict the characteristics and functions of complex networks. Many methods, ranging from the optimization-based algorithms to the heuristic-based algorithms, have been proposed for solving such a problem. Due to the inherent complexity of identifying network structure, how to design an effective algorithm with a higher accuracy and a lower computational cost still remains an open problem. Inspired by the computational capability and positive feedback mechanism in the wake of foraging process of Physarum, a kind of slime, a general Physarum-based computational framework for community detection is proposed in this paper. Based on the proposed framework, the inter-community edges can be identified from the intra-community edges in a network and the positive feedback of solving process in an algorithm can be further enhanced, which are used to improve the efficiency of original optimization-based and heuristic-based community detection algorithms, respectively. Some typical algorithms (e.g., genetic algorithm, ant colony optimization algorithm, and Markov clustering algorithm) and real-world datasets have been used to estimate the efficiency of our proposed computational framework. Experiments show that the algorithms optimized by Physarum-inspired computational framework perform better than the original ones, in terms of accuracy and computational cost.
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37
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Kushwaha N, Pant M, Kant S, Jain VK. Magnetic optimization algorithm for data clustering. Pattern Recognit Lett 2018. [DOI: 10.1016/j.patrec.2017.10.031] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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38
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39
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Zabihi F, Nasiri B. A Novel History-driven Artificial Bee Colony Algorithm for Data Clustering. Appl Soft Comput 2018. [DOI: 10.1016/j.asoc.2018.06.013] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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Das P, Das DK, Dey S. A modified Bee Colony Optimization (MBCO) and its hybridization with k-means for an application to data clustering. Appl Soft Comput 2018. [DOI: 10.1016/j.asoc.2018.05.045] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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41
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Jebari K, Elmoujahid A, Ettouhami A. Automatic Genetic Fuzzy c-Means. JOURNAL OF INTELLIGENT SYSTEMS 2018. [DOI: 10.1515/jisys-2018-0063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Abstract
Fuzzy c-means is an efficient algorithm that is amply used for data clustering. Nonetheless, when using this algorithm, the designer faces two crucial choices: choosing the optimal number of clusters and initializing the cluster centers. The two choices have a direct impact on the clustering outcome. This paper presents an improved algorithm called automatic genetic fuzzy c-means that evolves the number of clusters and provides the initial centroids. The proposed algorithm uses a genetic algorithm with a new crossover operator, a new mutation operator, and modified tournament selection; further, it defines a new fitness function based on three cluster validity indices. Real data sets are used to demonstrate the effectiveness, in terms of quality, of the proposed algorithm.
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Affiliation(s)
- Khalid Jebari
- Technologies and Sciences Faculty Tangier, Department of Computer Sciences, Tangier, Morocco
| | - Abdelaziz Elmoujahid
- LCS Laboratory, Faculty of Sciences, Department of Physics, Mohamed V University, Rabat, Morocco
| | - Aziz Ettouhami
- LCS Laboratory, Faculty of Sciences, Department of Physics, Mohamed V University, Rabat, Morocco
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Thalamala RC, Venkata Swamy Reddy A, Janet B. A Novel Bio-Inspired Algorithm Based on Social Spiders for Improving Performance and Efficiency of Data Clustering. JOURNAL OF INTELLIGENT SYSTEMS 2018. [DOI: 10.1515/jisys-2017-0178] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Abstract
Since the last decade, the collective intelligent behavior of groups of animals, birds or insects have attracted the attention of researchers. Swarm intelligence is the branch of artificial intelligence that deals with the implementation of intelligent systems by taking inspiration from the collective behavior of social insects and other societies of animals. Many meta-heuristic algorithms based on aggregative conduct of swarms through complex interactions with no supervision have been used to solve complex optimization problems. Data clustering organizes data into groups called clusters, such that each cluster has similar data. It also produces clusters that could be disjoint. Accuracy and efficiency are the important measures in data clustering. Several recent studies describe bio-inspired systems as information processing systems capable of some cognitive ability. However, existing popular bio-inspired algorithms for data clustering ignored good balance between exploration and exploitation for producing better clustering results. In this article, we propose a bio-inspired algorithm, namely social spider optimization (SSO), for clustering that maintains a good balance between exploration and exploitation using female and male spiders, respectively. We compare results of the proposed algorithm SSO with K means and other nature-inspired algorithms such as particle swarm optimization (PSO), ant colony optimization (ACO) and improved bee colony optimization (IBCO). We find it to be more robust as it produces better clustering results. Although SSO solves the problem of getting stuck in the local optimum, it needs to be modified for locating the best solution in the proximity of the generated global solution. Hence, we hybridize SSO with K means, which produces good results in local searches. We compare proposed hybrid algorithms SSO+K means (SSOKC), integrated SSOKC (ISSOKC), and interleaved SSOKC (ILSSOKC) with K means+PSO (KPSO), K means+genetic algorithm (KGA), K means+artificial bee colony (KABC) and interleaved K means+IBCO (IKIBCO) and find better clustering results. We use sum of intra-cluster distances (SICD), average cosine similarity, accuracy and inter-cluster distance to measure and validate the performance and efficiency of the proposed clustering techniques.
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Affiliation(s)
| | | | - B. Janet
- National Institute of Technology, Trichy, Tamil Nadu, India
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Qiao S, Zhou Y, Zhou Y, Wang R. A simple water cycle algorithm with percolation operator for clustering analysis. Soft comput 2018. [DOI: 10.1007/s00500-018-3057-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Kumar A, Kumar D, Jarial SK. A Review on Artificial Bee Colony Algorithms and Their Applications to Data Clustering. CYBERNETICS AND INFORMATION TECHNOLOGIES 2017. [DOI: 10.1515/cait-2017-0027] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Abstract
Data clustering is an important data mining technique being widely used in numerous applications. It is a method of creating groups (clusters) of objects, in such a way that objects in one cluster are very similar and objects in different clusters are quite distinct, i.e. intra-cluster distance is minimized and inter-cluster distance is maximized. However, the popular conventional clustering algorithms have shortcomings such as dependency on center initialization, slow convergence rate, local optima trap, etc. Artificial Bee Colony (ABC) algorithm is one of the popular swarm based algorithm inspired by intelligent foraging behaviour of honeybees that helps to minimize these shortcomings. In the past, many swarm intelligence based techniques for clustering were introduced and proved their performance. This paper provides a literature survey on ABC, its variants and its applications in data clustering.
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Affiliation(s)
- Ajit Kumar
- Deenbandhu Chhotu Ram University of Science and Technology , Murthal, India
| | - Dharmender Kumar
- Guru Jambheshwar University of Science and Technology , Hisar , India
| | - S. K. Jarial
- Deenbandhu Chhotu Ram University of Science and Technology , Murthal, India
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Özbakır L, Turna F. Clustering performance comparison of new generation meta-heuristic algorithms. Knowl Based Syst 2017. [DOI: 10.1016/j.knosys.2017.05.023] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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49
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Salama KM, Abdelbar AM. Learning cluster-based classification systems with ant colony optimization algorithms. SWARM INTELLIGENCE 2017. [DOI: 10.1007/s11721-017-0138-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Banharnsakun A. A MapReduce-based artificial bee colony for large-scale data clustering. Pattern Recognit Lett 2017. [DOI: 10.1016/j.patrec.2016.07.027] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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