A Clustering Algorithm Based on Improved Particle Swarm Optimization

2014 ◽  
Vol 635-637 ◽  
pp. 1467-1470
Author(s):  
Xun Wang
Keyword(s):  
Cluster Analysis ◽  
Particle Swarm Optimization ◽  
Local Search ◽  
Clustering Algorithm ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Initial Value ◽  
Swarm Optimization ◽  
Initial Cluster ◽  
Cluster Analysis Method

K-means algorithm is a traditional cluster analysis method, has the characteristics of simple ideas and algorithms, and thus become one of the commonly used methods of cluster analysis. However, the K-means algorithm classification results are too dependent on the choice of the initial cluster centers for some initial value, the algorithm may converge in general suboptimal solutions. Analysis of the K-means algorithm and particle swarm optimization based on a clustering algorithm based on improved particle swarm algorithm. The algorithm local search ability of the K-means algorithm and the global search ability of particle swarm optimization, local search ability to improve the K-means algorithm to accelerate the convergence speed effectively prevent the occurrence of the phenomenon of precocious puberty. The experiments show that the clustering algorithm has better convergence effect.

Clustering Algorithm Based on Improved Particle Swarm Algorithm

2013 ◽  
Vol 798-799 ◽  
pp. 689-692 ◽  
Author(s):  
Jin Hui Yang ◽  
Xi Cao
Keyword(s):  
Cluster Analysis ◽  
Particle Swarm Optimization ◽  
Local Search ◽  
Clustering Algorithm ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Initial Cluster ◽  
Cluster Analysis Method ◽  
Swarm Algorithm

K-means algorithm is a traditional cluster analysis method, has the characteristics of simple ideas and algorithms, and thus become one of the commonly used methods of cluster analysis. However, the K-means algorithm classification results are too dependent on the choice of the initial cluster centers for some initial value, the algorithm may converge in general suboptimal solutions. Analysis of the K-means algorithm and particle swarm optimization based on a clustering algorithm based on improved particle swarm algorithm. The algorithm local search ability of the K-means algorithm and the global search ability of particle swarm optimization, local search ability to improve the K-means algorithm to accelerate the convergence speed effectively prevent the occurrence of the phenomenon of precocious puberty. The experiments show that the clustering algorithm has better convergence effect.

scholarly journals Memetic Variable Clustering and Its Application

2019 ◽  
Vol 2019 ◽  
pp. 1-15
Author(s):  
JiaCheng Ni ◽  
Li Li
Keyword(s):  
Particle Swarm Optimization ◽  
Local Search ◽  
Clustering Analysis ◽  
Clustering Algorithm ◽  
Particle Swarm ◽  
Optimization Techniques ◽  
Experimental Result ◽  
Swarm Optimization ◽  
Metaheuristic Optimization ◽  
Variable Clustering

Clustering analysis is an important and difficult task in data mining and big data analysis. Although being a widely used clustering analysis technique, variable clustering did not get enough attention in previous studies. Inspired by the metaheuristic optimization techniques developed for clustering data items, we try to overcome the main shortcoming of k-means-based variable clustering algorithm, which is being sensitive to initial centroids by introducing the metaheuristic optimization. A novel memetic algorithm named MCLPSO (Memetic Comprehensive Learning Particle Swarm Optimization) based on CLPSO (Comprehensive Learning Particle Swarm Optimization) has been studied under the framework of memetic computing in our previous work. In this work, MCLPSO is used as a metaheuristic approach to improve the k-means-based variable clustering algorithm by adjusting the initial centroids iteratively to maximize the homogeneity of the clustering results. In MCLPSO, a chaotic local search operator is used and a simulated annealing- (SA-) based local search strategy is developed by combining the cognition-only PSO model with SA. The adaptive memetic strategy can enable the stagnant particles which cannot be improved by the comprehensive learning strategy to escape from the local optima and enable some elite particles to give fine-grained local search around the promising regions. The experimental result demonstrates a good performance of MCLPSO in optimizing the variable clustering criterion on several datasets compared with the original variable clustering method. Finally, for practical use, we also developed a web-based interactive software platform for the proposed approach and give a practical case study—analyzing the performance of semiconductor manufacturing system to demonstrate the usage.

A K-Medoids Clustering Algorithm Based on Particle Swarm Algorithm with Simulated Annealing

2013 ◽  
Vol 325-326 ◽  
pp. 1628-1631 ◽  
Author(s):  
Hong Zhou ◽  
Ke Luo
Keyword(s):  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Optimization Algorithm ◽  
Clustering Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Optimal Value ◽  
Swarm Algorithm

Be aimed at the problems that K-medoids algorithm is easy to fall into the local optimal value and basic particle swarm algorithm is easy to fall into the premature convergence, this paper joins the Simulated Annealing (SA) thought and proposes a novel K-medoids clustering algorithm based on Particle swarm optimization algorithm with simulated annealing. The new algorithm combines the quick optimization ability of particle swarm optimization algorithm and the probability of jumping property with SA, and maintains the characteristics that particle swarm algorithm is easy to realize, and improves the ability of the algorithm from local extreme value point. The experimental results show that the algorithm enhances the convergence speed and accuracy of the algorithm, and the clustering effect is better than the original k-medoids algorithm.

Clustering Algorithm Based on Improved Particle Swarm Optimization

2013 ◽  
Vol 765-767 ◽  
pp. 486-488
Author(s):  
Hong Chun Wang ◽  
Feng Wen Wen ◽  
Feng Song
Keyword(s):  
Cluster Analysis ◽  
Particle Swarm Optimization ◽  
Convergence Rate ◽  
Clustering Algorithm ◽  
Premature Convergence ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Initial Cluster ◽  
Area Of Influence ◽  
Strong Capacity

K-means algorithm has therefore become one of the methods widely used in cluster analysis. But the classification results of K-means algorithm depend on the initial cluster centers choice. We present a new neighborhood for PSO methods called the area of influence (AOI) and consider the combination of K-means has strong capacity of local searching and PSO has power global search ability. The improved PSO, i.e., improves the K-means local searching capacity, accelerates the convergence rate, and prevents the premature convergence effectively.

scholarly journals An Enhanced Multi-Objective Particle Swarm Optimization in Water Distribution Systems Design

Water ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 1334
Author(s):  
Mohamed R. Torkomany ◽  
Hassan Shokry Hassan ◽  
Amin Shoukry ◽  
Ahmed M. Abdelrazek ◽  
Mohamed Elkholy
Keyword(s):  
Particle Swarm Optimization ◽  
Local Search ◽  
Distribution Systems ◽  
Water Distribution ◽  
Water Distribution Systems ◽  
Particle Swarm ◽  
Large Network ◽  
Swarm Optimization ◽  
Original Algorithm ◽  
Multi Objective

The scarcity of water resources nowadays lays stress on researchers to develop strategies aiming at making the best benefit of the currently available resources. One of these strategies is ensuring that reliable and near-optimum designs of water distribution systems (WDSs) are achieved. Designing WDSs is a discrete combinatorial NP-hard optimization problem, and its complexity increases when more objectives are added. Among the many existing evolutionary algorithms, a new hybrid fast-convergent multi-objective particle swarm optimization (MOPSO) algorithm is developed to increase the convergence and diversity rates of the resulted non-dominated solutions in terms of network capital cost and reliability using a minimized computational budget. Several strategies are introduced to the developed algorithm, which are self-adaptive PSO parameters, regeneration-on-collision, adaptive population size, and using hypervolume quality for selecting repository members. A local search method is also coupled to both the original MOPSO algorithm and the newly developed one. Both algorithms are applied to medium and large benchmark problems. The results of the new algorithm coupled with the local search are superior to that of the original algorithm in terms of different performance metrics in the medium-sized network. In contrast, the new algorithm without the local search performed better in the large network.

Optimal Location and Size of Multiple Renewable Distributed Generation Units in Power Systems Using an Improved Version of Particle Swarm Optimization

2021 ◽  
pp. 15-27
Author(s):  
Mamdouh Kamaleldin AHMED ◽  
◽  
Mohamed Hassan OSMAN ◽  
Nikolay V. KOROVKIN ◽  
◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Power Systems ◽  
Distributed Generation ◽  
Particle Swarm ◽  
Optimal Location ◽  
Particle Swarm Algorithm ◽  
Power Losses ◽  
Negative Effects ◽  
Swarm Optimization ◽  
Distributed Generations

The penetration of renewable distributed generations (RDGs) such as wind and solar energy into conventional power systems provides many technical and environmental benefits. These benefits include enhancing power system reliability, providing a clean solution to rapidly increasing load demands, reducing power losses, and improving the voltage profile. However, installing these distributed generation (DG) units can cause negative effects if their size and location are not properly determined. Therefore, the optimal location and size of these distributed generations may be obtained to avoid these negative effects. Several conventional and artificial algorithms have been used to find the location and size of RDGs in power systems. Particle swarm optimization (PSO) is one of the most important and widely used techniques. In this paper, a new variant of particle swarm algorithm with nonlinear time varying acceleration coefficients (PSO-NTVAC) is proposed to determine the optimal location and size of multiple DG units for meshed and radial networks. The main objective is to minimize the total active power losses of the system, while satisfying several operating constraints. The proposed methodology was tested using IEEE 14-bus, 30-bus, 57-bus, 33-bus, and 69- bus systems with the change in the number of DG units from 1 to 4 DG units. The result proves that the proposed PSO-NTVAC is more efficient to solve the optimal multiple DGs allocation with minimum power loss and a high convergence rate.

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