Design of Minimum Cost Earthen Channels Having Side Slopes Riveted With Different Types of Riprap Stones and Unlined Bed by Using Particle Swarm Optimization

2016 ◽  
Vol 65 (3) ◽  
pp. 319-333
Author(s):  
S.K. Gupta ◽  
Umank Mishra ◽  
Vijay p. Singh
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Minimum Cost ◽  
Swarm Optimization ◽  
Different Types

scholarly journals Enhance Clustering Algorithm Using Optimization

2020 ◽  
Vol 3 (9) ◽  
pp. 136-142
Author(s):  
Roshankumar Ramashish Maurya ◽  
Anand Khandare
Keyword(s):  
Particle Swarm Optimization ◽  
Unsupervised Learning ◽  
Clustering Algorithm ◽  
Particle Swarm ◽  
Unsupervised Clustering ◽  
Classification Problems ◽  
Swarm Optimization ◽  
Different Types

Unsupervised learning can reveal the structure of datasets without being concerned with any labels, K-means clustering is one such method. Traditionally the initial clusters have been selected randomly, with the idea that the algorithm will generate better clusters. However, studies have shown there are methods to improve this initial clustering as well as the K-means process. This paper examines these results on different types of datasets to study if these results hold for all types of data. Another method that is used for unsupervised clustering is the algorithm based on Particle Swarm Optimization. For the second part this paper studies the classic K-means based algorithm and a Hybrid K-means algorithm which uses PSO to improve the results from K-means. The hybrid K-means algorithms are compared to the standard K-means clustering on two benchmark classification problems. In this project we used Kaggle dataset to with different size (small, large and medium) for comparison PSO, k-means and k-means hybrid.

Routing algorithm with QoS constraints for real time multicast communications using particle swarm optimization and genetic meta-optimizer

2016 ◽  
Vol 33 (2) ◽  
Author(s):  
YEISON JULIAN CAMARGO ◽  
Leonardo Juan Ramirez ◽  
Ana Karina Martinez
Keyword(s):  
Particle Swarm Optimization ◽  
Design Methodology ◽  
Multicast Routing ◽  
Routing Algorithm ◽  
Particle Swarm ◽  
Minimum Cost ◽  
Fine Tuning ◽  
Steiner Tree Problem ◽  
Swarm Optimization ◽  
Routing Problem

Purpose The current work shows an approach to solve the QoS multicast routing problem by using Particle Swarm Optimization (PSO). The problem of finding a route from a source node to multiple destination nodes (multicast) at a minimum cost is an NP-Complete problem (Steiner tree problem) and is even greater if Quality of Service -QoS- constraints are taken into account. Thus, approximation algorithms are necessary to solve this problem. This work presents a routing algorithm with two QoS constraints (delay and delay variation) for solving the routing problem based on a modified version of particle swarm optimization. Design/methodology/approach This work involved the following methodology: 1. Literature Review 2. Routing algorithm design 3. Implementation of the designed routing algorithm by java programming. 4. Simulations and results. Findings In this work we compared our routing algorithm against the exhaustive search approach. The results showed that our algorithm improves the execution times in about 40% with different topologies. Research limitations/implications The algorithm was tested in three different topologies with 30, 40 and 50 nodes with and a dense graph topology. Originality/value Our algorithm implements a novel technique for fine tuning the parameters of the implemented bio-inspired model (Particles Swarm Optimization) by using a Genetic Meta-Optimizer. We also present a simple and multi implementation approach by using an encoding system that fits multiple bio-inspired models.

scholarly journals Optimization of water distribution networks using a modified particle swarm optimization algorithm

2017 ◽  
Vol 18 (2) ◽  
pp. 660-678 ◽  
Author(s):  
Douglas F. Surco ◽  
Thelma P. B. Vecchi ◽  
Mauro A. S. S. Ravagnani
Keyword(s):  
Particle Swarm Optimization ◽  
Water Distribution ◽  
Large Scale ◽  
Particle Swarm ◽  
Minimum Cost ◽  
Distribution Networks ◽  
Water Distribution Networks ◽  
Benchmark Problems ◽  
Swarm Optimization ◽  
Local Optima

Abstract In the present work, a model is presented for the optimization of water distribution networks (WDN). The developed model can be used to verify node pressures, head losses, and fluid flow rate and velocity in each pipe. The algorithm is based on particle swarm optimization (PSO), considering real and discrete variables and avoiding premature convergence to local optima using objective function penalization. The model yields the minimum cost of the network, the node pressures and the velocities in the pipes. The pressures and velocities are calculated using the hydraulic simulator Epanet. Some benchmark problems are used to test the applicability of the developed model, considering WDN for small-, medium-, and large-scale problems. Obtained results are consistent with those found in the literature.

scholarly journals Particle Swarm Optimization for Multiband Metamaterial Fractal Antenna

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Balamati Choudhury ◽  
Sangeetha Manickam ◽  
R. M. Jha
Keyword(s):  
Particle Swarm Optimization ◽  
Patch Antenna ◽  
Particle Swarm ◽  
Fractal Antenna ◽  
Performance Parameters ◽  
Self Similarity ◽  
Swarm Optimization ◽  
X Band ◽  
Different Types ◽  
Miniaturized Antenna

The property of self-similarity, recursive irregularity, and space filling capability of fractal antennas makes it useful for various applications in wireless communication, including multiband miniaturized antenna designs. In this paper, an effort has been made to use the metamaterial structures in conjunction with the fractal patch antenna, which resonates at six different frequencies covering both C and X band. Two different types of square SRR are loaded on the fractal antenna for this purpose. Particle swarm optimization (PSO) is used for optimization of these metamaterial structures. The optimized metamaterial structures, after loading upon, show significant increase in performance parameters such as bandwidth, gain, and directivity.

scholarly journals A Dynamic Multi-Swarm Particle Swarm Optimization with Global Detection Mechanism

2021 ◽  
Vol 15 (4) ◽  
pp. 0-0
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Superior Performance ◽  
Detection Mechanism ◽  
Swarm Optimization ◽  
Information Interaction ◽  
Standard Particle Swarm Optimization ◽  
Comparison Results ◽  
Different Types ◽  
Average Fitness

To overcome the shortcomings of the standard particle swarm optimization algorithm (PSO), such as premature convergence and low precision, a dynamic multi-swarm PSO with global detection mechanism (DMS-PSO-GD) is proposed. In DMS-PSO-GD, the whole population is divided into two kinds of sub-swarms: several same-sized dynamic sub-swarms and a global sub-swarm. The dynamic sub-swarms achieve information interaction and sharing among themselves through the randomly regrouping strategy. The global sub-swarm evolves independently and learns from the optimal individuals of the dynamic sub-swarm with dominant characteristics. During the evolution process of the population, the variances and average fitness values of dynamic sub-swarms are used for measuring the distribution of the particles, by which the dominant one and the optimal individual can be detected easily. The comparison results among DMS-PSO-GD and other 5 well-known algorithms suggest that it demonstrates superior performance for solving different types of functions.

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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