Parallel Particle Swarm Optimization Methods for Graph Drawing

2013 ◽  
Vol 5 (10) ◽  
pp. 956-964
Author(s):  
Jianhua Qu ◽  
Yi Song ◽  
Stphane Bressan
Keyword(s):  
Particle Swarm Optimization ◽  
Graph Drawing ◽  
Particle Swarm ◽  
Optimization Methods ◽  
Swarm Optimization ◽  
Parallel Particle Swarm Optimization

scholarly journals GPU-Based Parallel Particle Swarm Optimization Methods for Graph Drawing

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Jianhua Qu ◽  
Xiyu Liu ◽  
Minghe Sun ◽  
Feng Qi
Keyword(s):  
Particle Swarm Optimization ◽  
Large Scale ◽  
Graph Drawing ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Optimization Methods ◽  
Population Based ◽  
Pictorial Representation ◽  
Swarm Optimization ◽  
Wide Range

Particle Swarm Optimization (PSO) is a population-based stochastic search technique for solving optimization problems, which has been proven to be effective in a wide range of applications. However, the computational efficiency on large-scale problems is still unsatisfactory. A graph drawing is a pictorial representation of the vertices and edges of a graph. Two PSO heuristic procedures, one serial and the other parallel, are developed for undirected graph drawing. Each particle corresponds to a different layout of the graph. The particle fitness is defined based on the concept of the energy in the force-directed method. The serial PSO procedure is executed on a CPU and the parallel PSO procedure is executed on a GPU. Two PSO procedures have different data structures and strategies. The performance of the proposed methods is evaluated through several different graphs. The experimental results show that the two PSO procedures are both as effective as the force-directed method, and the parallel procedure is more advantageous than the serial procedure for larger graphs.

OPTIMAL PREDICTION AND DESIGN OF SURFACE ROUGHNESS FOR CNC TURNING OF AL7075-T6 BY USING THE TAGUCHI HYBRID QPSO ALGORITHM

2016 ◽  
Vol 40 (5) ◽  
pp. 883-895 ◽  
Author(s):  
Wen-Jong Chen ◽  
Chuan-Kuei Huang ◽  
Qi-Zheng Yang ◽  
Yin-Liang Yang
Keyword(s):  
Surface Roughness ◽  
Particle Swarm Optimization ◽  
Predictive Models ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Cutting Parameters ◽  
Optimization Methods ◽  
Swarm Optimization ◽  
Cnc Turning ◽  
Multi Objective

This paper combines the Taguchi-based response surface methodology (RSM) with a multi-objective hybrid quantum-behaved particle swarm optimization (MOHQPSO) to predict the optimal surface roughness of Al7075-T6 workpiece through a CNC turning machining. First, the Taguchi orthogonal array L27 (36) was applied to determine the crucial cutting parameters: feed rate, tool relief angle, and cutting depth. Subsequently, the RSM was used to construct the predictive models of surface roughness (Ra, Rmax, and Rz). Finally, the MOHQPSO with mutation was used to determine the optimal roughness and cutting conditions. The results show that, compared with the non-optimization, Taguchi and classical multi-objective particle swarm optimization methods (MOPSO), the roughness Ra using MOHQPSO along the Pareto optimal solution are improved by 68.24, 59.31 and 33.80%, respectively. This reveals that the predictive models established can improve the machining quality in CNC turning of Al7075-T6.

scholarly journals Anti-synchronization of discrete-time chaotic systems using optimization algorithms

2011 ◽  
pp. 143-147
Author(s):  
Mahdieh Adeli ◽  
Hassan Zarabadipoor
Keyword(s):  
Particle Swarm Optimization ◽  
Pid Control ◽  
Pid Controller ◽  
Chaotic Systems ◽  
Optimization Algorithms ◽  
Particle Swarm ◽  
Optimization Methods ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Modified Particle Swarm Optimization

In this paper, anti-synchronization of discrete chaotic system based on optimization algorithms are investigated. Different controllers have been used for anti-synchronization of two identical discrete chaotic systems. A proportional-integral-derivative (PID) control is used and its parameters is tuned by the four optimization algorithms, such as genetic algorithm (GA), particle swarm optimization (PSO), modified particle swarm optimization (MPSO) and improved particle swarm optimization (IPSO). Simulation results of these optimization methods to determine the PID controller parameters to anti-synchronization of two chaotic systems are compared. Numerical results show that the improved particle swarm optimization has the best result.

PERFORMANCE EVALUATION OF PARALLEL GENETIC AND PARTICLE SWARM OPTIMIZATION ALGORITHMS WITHIN THE MULTICORE ARCHITECTURE

2014 ◽  
Vol 13 (04) ◽  
pp. 1450024
Author(s):  
A. S. RADHAMANI ◽  
E. BABURAJ
Keyword(s):  
Particle Swarm Optimization ◽  
Performance Optimization ◽  
Particle Swarm ◽  
Optimization Methods ◽  
Swarm Optimization ◽  
Multicore Architecture ◽  
Bacterial Foraging ◽  
Processor Performance ◽  
Almost All ◽  
Time And Energy

In recent studies we found that there are many optimization methods presented for multicore processor performance optimization, however each method is suffered from limitations. Hence in this paper we presented a new method which is a combination of bacterial Foraging Particle swarm Optimization with certain constraints named as Constraint based Bacterial Foraging Particle Swarm Optimization (CBFPSO) scheduling can be effectively implemented. The proposed Constraint based Bacterial Foraging Particle Swarm Optimization (CBFPSO) scheduling for multicore architecture, which updates the velocity and position by two bacterial behaviours, i.e. reproduction and elimination dispersal. The performance of CBFPSO is compared with the simulation results of GA, and the result shows that the proposed algorithm has pretty good performance on almost all types of cores compared to GA with respect to completion time and energy consumption.

scholarly journals Effective Approach to Classify and Segment Retinal Hemorrhage Using ANFIS and Particle Swarm Optimization

2018 ◽  
Vol 27 (4) ◽  
pp. 681-697
Author(s):  
Lawrence Livingston Godlin Atlas ◽  
Kumar Parasuraman
Keyword(s):  
Particle Swarm Optimization ◽  
Median Filter ◽  
Particle Swarm ◽  
Optimization Methods ◽  
Retinal Hemorrhage ◽  
Scale Invariant ◽  
Swarm Optimization ◽  
Segmented Images ◽  
Occurrence Matrix ◽  
Scale Invariant Feature

Abstract The main objective of this study is to progress the structure and segment the images from hemorrhage recognition in retinal fundus images in ostensible. The abnormal bleeding of blood vessels in the retina which is the membrane in the back of the eye is called retinal hemorrhage. The image folders are deliberated, and the filter technique is utilized to decrease the images specifically adaptive median filter in our suggested proposal. Gray level co-occurrence matrix (GLCM), grey level run length matrix (GLRLM) and Scale invariant feature transform (SIFT) feature skills are present after filtrating the feature withdrawal. After this, the organization technique is performed, specifically artificial neural network with fuzzy interface system (ANFIS) method; with the help of this organization, exaggerated and non-affected images are categorized. Affected hemorrhage images are transpired for segmentation procedure, and in this exertion, threshold optimization is measured with numerous optimization methods; on the basis of this, particle swarm optimization is accomplished in improved manner. Consequently, the segmented images are projected, and the sensitivity is great when associating with accurateness and specificity in the MATLAB platform.

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