scholarly journals A Statistical Analysis of Particle Swarm Optimization With and Without Digital Pheromones

2007 ◽  
Author(s):  
Vijay Kalivarapu ◽  
Eliot Winer
Keyword(s):  
Particle Swarm Optimization ◽  
Statistical Analysis ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Digital Pheromones

scholarly journals Fuzzy adaptive catfish particle swarm optimization

2012 ◽  
Vol 1 (2) ◽  
pp. 149 ◽  
Author(s):  
Li-Yeh Chuang ◽  
Sheng-Wei Tsai ◽  
Cheng-Hong Yang
Keyword(s):  
Particle Swarm Optimization ◽  
Statistical Analysis ◽  
Particle Swarm ◽  
Test Functions ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
Interactive Behavior ◽  
Different Dimensions ◽  
Swarm Intelligence Optimization ◽  
Fuzzy Adaptive

The catfish particle swarm optimization (CatfishPSO) algorithm is a novel swarm intelligence optimization technique.This algorithm was inspired by the interactive behavior of sardines and catfish. The observed catfish effect is applied toimprove the performance of particle swarm optimization (PSO). In this paper, we propose fuzzy CatfishPSO(F-CatfishPSO), which uses fuzzy to dynamically change the inertia weight of CatfishPSO. Ten benchmark functions with10, 20, and 30 different dimensions were selected as the test functions. Statistical analysis of the experimental resultsindicates that F-CatfishPSO outperformed PSO, F-PSO and CatfishPSO.

The Parameter Estimation and Statistical Analysis of Constraint Optimization Penalty Function that Solved by Particle Swarm Optimization

2013 ◽  
Vol 791-793 ◽  
pp. 1273-1277
Author(s):  
Ming Tao Wu ◽  
Yong Yang
Keyword(s):  
Parameter Estimation ◽  
Particle Swarm Optimization ◽  
Statistical Analysis ◽  
Penalty Function ◽  
Particle Swarm ◽  
Constraint Optimization ◽  
Swarm Optimization ◽  
Test Function

Particle swarm optimization has been successfully applied to optimization, however it was not effective on all of the constraint functions. This paper has validated it through five measures and analyzed the result of test function. From the result we could find out which was the best one or which was the worst one, then we use four methods of Punishment strategy function and each of them was tested by four particle swarm optimization so that we could know which one is the best to be combined with particle swarm optimization, and it will be explained theoretically.

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