53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference<BR&amp;gt;20th AIAA/ASME/AHS Adapti 2012
DOI: 10.2514/6.2012-1678
|View full text |Cite
|
Sign up to set email alerts
|

Avoiding Premature Convergence in a Mixed-Discrete Particle Swarm Optimization (MDPSO) Algorithm

Abstract: Over the past decade or so, Particle Swarm Optimization (PSO) has emerged to be one of most useful methodologies to address complex high dimensional optimization problemsit's popularity can be attributed to its ease of implementation, and fast convergence property (compared to other population based algorithms). However, a premature stagnation of candidate solutions has been long standing in the way of its wider application, particularly to constrained single-objective problems. This issue becomes all the more… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
8
0

Year Published

2012
2012
2024
2024

Publication Types

Select...
6
3

Relationship

3
6

Authors

Journals

citations
Cited by 11 publications
(8 citation statements)
references
References 28 publications
0
8
0
Order By: Relevance
“…Sun et al [191] introduced a constraint preserving mechanism in PSO (CPMPSO) to solve mixed-variable optimization problems and reported competitive results when tested on two real-world mixed-variable optimization problems. Chowdhury et al [192] considered the issue of premature stagnation of candidate solutions especially in single objective, constrained problems when using PSO and noted its pronounced effect in objective functions that make use of a mixture of continuous and discrete design variables. In order to address this issue, the authors proposed a modification in PSO which made use of continuous optimization as its primary strategy and subsequently a nearest vertex approximation criterion for updating of discrete variables.…”
Section: Hybrid Approachesmentioning
confidence: 99%
“…Sun et al [191] introduced a constraint preserving mechanism in PSO (CPMPSO) to solve mixed-variable optimization problems and reported competitive results when tested on two real-world mixed-variable optimization problems. Chowdhury et al [192] considered the issue of premature stagnation of candidate solutions especially in single objective, constrained problems when using PSO and noted its pronounced effect in objective functions that make use of a mixture of continuous and discrete design variables. In order to address this issue, the authors proposed a modification in PSO which made use of continuous optimization as its primary strategy and subsequently a nearest vertex approximation criterion for updating of discrete variables.…”
Section: Hybrid Approachesmentioning
confidence: 99%
“…By contrast, the latter method solves a problem with a collection of solutions. The nature of population-oriented algorithms makes them more reliable due to the higher probability of local optima avoidance [3]. However, they need more…”
Section: Accepted Manuscriptmentioning
confidence: 99%
“…Further discussion of the estimation of the population diversity and the formulation of the diversity coefficient γ can be found in Ref. [27,28].…”
Section: Mixed-discrete Particle Swarm Optimizationmentioning
confidence: 99%