Statistical analysis for vortex particle swarm optimization

2018 ◽  
Vol 67 ◽  
pp. 370-386 ◽  
Author(s):  
Helbert Eduardo Espitia ◽  
Jorge Iván Sofrony
Keyword(s):  
Particle Swarm Optimization ◽  
Statistical Analysis ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Vortex Particle

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.

A Particle Swarm Optimization-Based Approach for Finding Reliability in a Total Hip Prosthesis

2021 ◽  
pp. 222-242
Author(s):  
Bouakkar Loubna ◽  
Ameddah Hacene ◽  
Mazouz Hammoud
Keyword(s):  
Particle Swarm Optimization ◽  
Statistical Analysis ◽  
Optimization Problems ◽  
Hip Prosthesis ◽  
Particle Swarm ◽  
Reliability Optimization ◽  
Design Phase ◽  
Total Hip Prosthesis ◽  
Swarm Optimization ◽  
Total Hip

Nowadays, we assist the global extension of reliability optimization problems from the design phase of systems and sub-systems to the design and operational phases, not only of systems and sub-systems, but also of bio functionality design. This chapter investigates the relative performances of particle swarm optimization (PSO) variants when used to find reliability in the total hip prosthesis by finding the maximization of jumping distance (JD) to avoid dislocation and the minimization of system's stability to offer mobility. Statistical analysis of different cases of head diameters of 22, 28, 36, 40 mm has been conducted to survey the convergence and relative performances of the main PSO variants when applied to solve reliability in the total hip prosthesis.

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