Empirical computation of the quasi-optimal number of informants in particle swarm optimization

2011 ◽  
Author(s):  
José García-Nieto ◽  
Enrique Alba
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Optimal Number ◽  
Swarm Optimization

scholarly journals A Hyper Heuristics Technique for Data Partitioning and Scheduling to Heterogeneous Systems using Genetic Algorithm and Improved Particle Swarm Optimization

2019 ◽  
Vol 8 (2S8) ◽  
pp. 1696-1701
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Scheduling Algorithm ◽  
Particle Swarm ◽  
Optimal Number ◽  
Data Partitioning ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Fitness Value ◽  
Real Coded Genetic Algorithm

Development of the load partitioning for multiple round load distribution and effective scheduling of partitioned load to heterogeneous processor is primary goal of distributed and parallel system. In this paper, we propose hyper heuristics scheduling algorithm for load partitioning using genetic and improved particle swarm optimization techniques.A communication model is used to predict the optimal activation order, optimal number of processor and optimal number of rounds of the load. Heuristics Based Scheduling Algorithm is proposed using Hyper Heuristic Scheduling which is used to find the candidate solution (low level heuristic) to form Scheduling Solutions (heuristics algorithms) for large scale system with diversity operator as sequence dependent and sequence independent scheduling. For this solution, processing time of the entire processing load will be reduced. Hybrid Real Code genetic algorithm(HRGA) computes optimal activation order with cross over and mutation operator without considering the processor latency and different types of variation in the perturbation parameters. In order to optimize this issue, we utilizeImproved Particle swarm optimization (IPSO) determine the load fraction for generating activation order in terms of dynamically predicting fitness value of the processor with certain number. The Simulation analysis demonstrates the proposed model performance in terms of mean, standard deviation, computational complexity and Average Execution Time comparing against hybrid real coded genetic algorithm

scholarly journals Blockchain-IoT peer device storage optimization using an advanced time-variant multi-objective particle swarm optimization algorithm

2022 ◽  
Vol 2022 (1) ◽  
Author(s):  
Clement Nartey ◽  
Eric Tutu Tchao ◽  
James Dzisi Gadze ◽  
Bright Yeboah-Akowuah ◽  
Henry Nunoo-Mensah ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Optimal Number ◽  
Hybrid Architecture ◽  
Swarm Optimization ◽  
Solution Approach ◽  
Multi Objective ◽  
Probability Optimization

AbstractThe integration of Internet of Things devices onto the Blockchain implies an increase in the transactions that occur on the Blockchain, thus increasing the storage requirements. A solution approach is to leverage cloud resources for storing blocks within the chain. The paper, therefore, proposes two solutions to this problem. The first being an improved hybrid architecture design which uses containerization to create a side chain on a fog node for the devices connected to it and an Advanced Time-variant Multi-objective Particle Swarm Optimization Algorithm (AT-MOPSO) for determining the optimal number of blocks that should be transferred to the cloud for storage. This algorithm uses time-variant weights for the velocity of the particle swarm optimization and the non-dominated sorting and mutation schemes from NSGA-III. The proposed algorithm was compared with results from the original MOPSO algorithm, the Strength Pareto Evolutionary Algorithm (SPEA-II), and the Pareto Envelope-based Selection Algorithm with region-based selection (PESA-II), and NSGA-III. The proposed AT-MOPSO showed better results than the aforementioned MOPSO algorithms in cloud storage cost and query probability optimization. Importantly, AT-MOPSO achieved 52% energy efficiency compared to NSGA-III. To show how this algorithm can be applied to a real-world Blockchain system, the BISS industrial Blockchain architecture was adapted and modified to show how the AT-MOPSO can be used with existing Blockchain systems and the benefits it provides.

scholarly journals Particle Swarm Optimization Based Fuzzy Clustering Approach to Identify Optimal Number of Clusters

2014 ◽  
Vol 4 (1) ◽  
pp. 43-56 ◽  
Author(s):  
Min Chen ◽  
Simone A. Ludwig
Keyword(s):  
Cluster Analysis ◽  
Particle Swarm Optimization ◽  
Fuzzy Clustering ◽  
Particle Swarm ◽  
Optimal Number ◽  
Swarm Optimization ◽  
Number Of Clusters ◽  
Sammon Mapping ◽  
Clustering Approach ◽  
Optimal Number Of Clusters

Abstract Fuzzy clustering is a popular unsupervised learning method that is used in cluster analysis. Fuzzy clustering allows a data point to belong to two or more clusters. Fuzzy c-means is the most well-known method that is applied to cluster analysis, however, the shortcoming is that the number of clusters need to be predefined. This paper proposes a clustering approach based on Particle Swarm Optimization (PSO). This PSO approach determines the optimal number of clusters automatically with the help of a threshold vector. The algorithm first randomly partitions the data set within a preset number of clusters, and then uses a reconstruction criterion to evaluate the performance of the clustering results. The experiments conducted demonstrate that the proposed algorithm automatically finds the optimal number of clusters. Furthermore, to visualize the results principal component analysis projection, conventional Sammon mapping, and fuzzy Sammon mapping were used

Quantum-Behaved Particle Swarm Optimization Dynamic Clustering Algorithm

2013 ◽  
Vol 694-697 ◽  
pp. 2757-2760 ◽  
Author(s):  
Chun Yan Zhang ◽  
Wei Chen
Keyword(s):  
Particle Swarm Optimization ◽  
Clustering Algorithm ◽  
Learning Strategy ◽  
Clustering Algorithms ◽  
Particle Swarm ◽  
Optimal Number ◽  
Data Sets ◽  
Dynamic Clustering ◽  
Swarm Optimization ◽  
Coding Method

This paper proposed a revised quantum-behaved particle swarm optimization algorithm utilizing comprehensive learning strategy to prevent the universal tendency of premature convergence, based on which introduced a novel data clustering algorithm as well. The optimal number of cluster could be automatically obtained by this novel clustering algorithm because a new special coding method for particles was used. Compared with another two dynamic clustering algorithms on five testing data sets, the proposed dynamic clustering algorithm based on the comprehensive learning strategy has the best performance and with the best potential application prospect.

Quantum-Behaved Particle Swarm Optimization Based Radial Basis Function Network for Classification of Clinical Datasets

2020 ◽  
pp. 1290-1313 ◽  
Author(s):  
N. Leema ◽  
H. Khanna Nehemiah ◽  
A. Kannan
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Radial Basis Function ◽  
Basis Function ◽  
Clustering Algorithm ◽  
Particle Swarm ◽  
Optimal Number ◽  
Swarm Optimization ◽  
Classification Framework ◽  
Radial Basis

In this article, a classification framework that uses quantum-behaved particle swarm optimization neural network (QPSONN) classifiers for diagnosing a disease is discussed. The neural network used for classification is radial basis function neural network (RBFNN). For training the RBFNN K-means clustering algorithm and quantum-behaved particle swarm optimization (QPSO) algorithm has been used. The K-means clustering algorithm is used to find the optimal number of clusters which determines the number of neurons in the hidden layer. The cluster approximation error is used to find the optimal clusters. The weights between the hidden and the output layer is determined using QPSO algorithm based on the mean squared error (MSE). The performance of the developed classifier model has been tested with five clinical datasets, namely Pima Indian Diabetes, Hepatitis, Bupa Liver Disease, Wisconsin Breast Cancer and Cleveland Heart Disease were obtained from the University of California, Irvine (UCI) machine learning repository.

Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

2020 ◽  
Vol 39 (4) ◽  
pp. 5699-5711
Author(s):  
Shirong Long ◽  
Xuekong Zhao
Keyword(s):  
Feature Extraction ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Real Time ◽  
Image Recognition ◽  
Particle Swarm ◽  
Learning Technology ◽  
Search Performance ◽  
Swarm Optimization ◽  
Teaching Mode

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.

Sistem Kontrol Optimal Fuzzy-Particle Swarm Optimization

2018 ◽  
Vol 9 (1) ◽  
pp. 1-5
Author(s):  
Fachrudin Hunaini ◽  
Imam Robandi ◽  
Nyoman Sutantra
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Control System ◽  
Membership Function ◽  
Fuzzy Logic Control ◽  
Particle Swarm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Motion Error ◽  
Logic Control

Fuzzy Logic Control (FLC) is a reliable control system for controlling nonlinear systems, but to obtain optimal fuzzy logic control results, optimal Membership Function parameters are needed. Therefore in this paper Particle Swarm Optimization (PSO) is used as a fast and accurate optimization method to determine Membership Function parameters. The optimal control system simulation is carried out on the automatic steering system of the vehicle model and the results obtained are the vehicle's lateral motion error can be minimized so that the movement of the vehicle can always be maintained on the expected trajectory

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