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.

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.

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.

scholarly journals Improving DFT-Based Image Watermarking Using Particle Swarm Optimization Algorithm

Mathematics ◽  
2021 ◽  
Vol 9 (15) ◽  
pp. 1795
Author(s):  
Manuel Cedillo-Hernandez ◽  
Antonio Cedillo-Hernandez ◽  
Francisco J. Garcia-Ugalde
Keyword(s):  
Fourier Transform ◽  
Particle Swarm Optimization ◽  
Discrete Fourier Transform ◽  
Image Watermarking ◽  
Particle Swarm ◽  
Optimization Techniques ◽  
Robust Watermarking ◽  
Swarm Optimization ◽  
General Terms ◽  
Operation Parameters

Robust digital image watermarking is an information security technique that has been widely used to solve several issues related mainly with copyright protection as well as ownership authentication. In general terms, robust watermarking conceals a small signal called a “watermark” in a host image in a form imperceptible to human vision. The efficiency of conventional robust watermarking based on frequency domain depend directly on the results of performance in terms of robustness and imperceptibility. According to the application scenario and the image dataset, it is common practice to adjust the key parameters used by robust watermarking methods in an experimental form; however, this manual adjustment may involve exhaustive tasks and at the same time be a drawback in practical scenarios. In recent years, several optimization techniques have been adopted by robust watermarking to allowing adjusting in an automatic form its key operation parameters, improving thus its performance. In this context, this paper proposes an improved robust watermarking algorithm in discrete Fourier transform via spread spectrum, optimizing the key operation parameters, particularly the amounts of bands and coefficients of frequency as well as the watermark strength factor using particle swarm optimization in conjunction with visual information fidelity and bit correct rate criteria. Experimental results obtained in this research show improved robustness against common signal processing and geometric distortions, preserving a high visual quality in color images. Performance comparison with conventional discrete Fourier transform proposal is provided, as well as with the current state-of-the-art of particle swarm optimization applied to image watermarking.

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.

scholarly journals Asteroid Rendezvous Mission Design Using Multiobjective Particle Swarm Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Ya-zhong Luo ◽  
Li-ni Zhou
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Optimization Techniques ◽  
Mission Design ◽  
Trajectory Design ◽  
Time Transfer ◽  
Pareto Solution ◽  
Swarm Optimization ◽  
Multiobjective Particle Swarm Optimization ◽  
Asteroid Mission

A new preliminary trajectory design method for asteroid rendezvous mission using multiobjective optimization techniques is proposed. This method can overcome the disadvantages of the widely employed Pork-Chop method. The multiobjective integrated launch window and multi-impulse transfer trajectory design model is formulated, which employes minimum-fuel cost and minimum-time transfer as two objective functions. The multiobjective particle swarm optimization (MOPSO) is employed to locate the Pareto solution. The optimization results of two different asteroid mission designs show that the proposed approach can effectively and efficiently demonstrate the relations among the mission characteristic parameters such as launch time, transfer time, propellant cost, and number of maneuvers, which will provide very useful reference for practical asteroid mission design. Compared with the PCP method, the proposed approach is demonstrated to be able to provide much more easily used results, obtain better propellant-optimal solutions, and have much better efficiency. The MOPSO shows a very competitive performance with respect to the NSGA-II and the SPEA-II; besides a proposed boundary constraint optimization strategy is testified to be able to improve its performance.

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