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Zhang D, Ma G, Deng Z, Wang Q, Zhang G, Zhou W. A self-adaptive gradient-based particle swarm optimization algorithm with dynamic population topology. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Tian H, Guo J, Xiao H, Yan K, Sato Y. An electronic transition-based bare bones particle swarm optimization algorithm for high dimensional optimization problems. PLoS One 2022; 17:e0271925. [PMID: 35877651 PMCID: PMC9312387 DOI: 10.1371/journal.pone.0271925] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 07/10/2022] [Indexed: 11/19/2022] Open
Abstract
An electronic transition-based bare bones particle swarm optimization (ETBBPSO) algorithm is proposed in this paper. The ETBBPSO is designed to present high precision results for high dimensional single-objective optimization problems. Particles in the ETBBPSO are divided into different orbits. A transition operator is proposed to enhance the global search ability of ETBBPSO. The transition behavior of particles gives the swarm more chance to escape from local minimums. In addition, an orbit merge operator is proposed in this paper. An orbit with low search ability will be merged by an orbit with high search ability. Extensive experiments with CEC2014 and CEC2020 are evaluated with ETBBPSO. Four famous population-based algorithms are also selected in the control group. Experimental results prove that ETBBPSO can present high precision results for high dimensional single-objective optimization problems.
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Affiliation(s)
- Hao Tian
- School of Information and Communication Engineering, Hubei University of Economics, Wuhan, China
| | - Jia Guo
- School of Information and Communication Engineering, Hubei University of Economics, Wuhan, China
- * E-mail:
| | - Haiyang Xiao
- School of Information and Communication Engineering, Hubei University of Economics, Wuhan, China
| | - Ke Yan
- Smart Business Department of China Construction Third Engineering Bureau Installation Engineering Co., Ltd., Wuhan, China
| | - Yuji Sato
- Faculty of Computer and Information Sciences, Hosei University, Tokyo, Japan
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Kaur A, Kumar Y. Neighborhood search based improved bat algorithm for data clustering. APPL INTELL 2022. [DOI: 10.1007/s10489-021-02934-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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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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An Empirical Study of Cluster-Based MOEA/D Bare Bones PSO for Data Clustering †. ALGORITHMS 2021. [DOI: 10.3390/a14110338] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Previously, cluster-based multi or many objective function techniques were proposed to reduce the Pareto set. Recently, researchers proposed such techniques to find better solutions in the objective space to solve engineering problems. In this work, we applied a cluster-based approach for solution selection in a multiobjective evolutionary algorithm based on decomposition with bare bones particle swarm optimization for data clustering and investigated its clustering performance. In our previous work, we found that MOEA/D with BBPSO performed the best on 10 datasets. Here, we extend this work applying a cluster-based approach tested on 13 UCI datasets. We compared with six multiobjective evolutionary clustering algorithms from the existing literature and ten from our previous work. The proposed technique was found to perform well on datasets highly overlapping clusters, such as CMC and Sonar. So far, we found only one work that used cluster-based MOEA for clustering data, the hierarchical topology multiobjective clustering algorithm. All other cluster-based MOEA found were used to solve other problems that are not data clustering problems. By clustering Pareto solutions and evaluating new candidates against the found cluster representatives, local search is introduced in the solution selection process within the objective space, which can be effective on datasets with highly overlapping clusters. This is an added layer of search control in the objective space. The results are found to be promising, prompting different areas of future research which are discussed, including the study of its effects with an increasing number of clusters as well as with other objective functions.
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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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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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Guo J, Sato Y. A fission-fusion hybrid bare bones particle swarm optimization algorithm for single-objective optimization problems. APPL INTELL 2019. [DOI: 10.1007/s10489-019-01474-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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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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Automatic clustering based on density peak detection using generalized extreme value distribution. Soft comput 2017. [DOI: 10.1007/s00500-017-2748-7] [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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Cooperation coevolution with fast interdependency identification for large scale optimization. Inf Sci (N Y) 2017. [DOI: 10.1016/j.ins.2016.11.013] [Citation(s) in RCA: 59] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Yan D, Lu Y, Levy D. Parameter identification of robot manipulators: a heuristic particle swarm search approach. PLoS One 2015; 10:e0129157. [PMID: 26039090 PMCID: PMC4454697 DOI: 10.1371/journal.pone.0129157] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2014] [Accepted: 05/05/2015] [Indexed: 11/18/2022] Open
Abstract
Parameter identification of robot manipulators is an indispensable pivotal process of achieving accurate dynamic robot models. Since these kinetic models are highly nonlinear, it is not easy to tackle the matter of identifying their parameters. To solve the difficulty effectively, we herewith present an intelligent approach, namely, a heuristic particle swarm optimization (PSO) algorithm, which we call the elitist learning strategy (ELS) and proportional integral derivative (PID) controller hybridized PSO approach (ELPIDSO). A specified PID controller is designed to improve particles' local and global positions information together with ELS. Parameter identification of robot manipulators is conducted for performance evaluation of our proposed approach. Experimental results clearly indicate the following findings: Compared with standard PSO (SPSO) algorithm, ELPIDSO has improved a lot. It not only enhances the diversity of the swarm, but also features better search effectiveness and efficiency in solving practical optimization problems. Accordingly, ELPIDSO is superior to least squares (LS) method, genetic algorithm (GA), and SPSO algorithm in estimating the parameters of the kinetic models of robot manipulators.
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Affiliation(s)
- Danping Yan
- College of Public Administration, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Non-traditional Security Center of Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Yongzhong Lu
- School of Software Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China
- * E-mail:
| | - David Levy
- Faculty of Engineering and Information Technologies, University of Sydney, Sydney, New South Wales, Australia
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Zouache D, Nouioua F, Moussaoui A. Quantum-inspired firefly algorithm with particle swarm optimization for discrete optimization problems. Soft comput 2015. [DOI: 10.1007/s00500-015-1681-x] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Mahdavi S, Shiri ME, Rahnamayan S. Metaheuristics in large-scale global continues optimization: A survey. Inf Sci (N Y) 2015. [DOI: 10.1016/j.ins.2014.10.042] [Citation(s) in RCA: 281] [Impact Index Per Article: 31.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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