Paper Info
Title
Hybridizing Particle Filters and Population-based Metaheuristics for Dynamic Optimization Problems
Abstract
Many real-world optimization problems are dynamic. These problems require from powerful methods to adapt to problem modifications over time. Most applied research on metaheuristics has focused on static (non-changing) optimization problems and these methods often lack from adaptation strategies. Particle filters are sequential Monte Carlo estimation methods which can be applied to Bayesian filtering for nonlinear and non-Gaussian discrete-time dynamic models. In this paper, we propose a general method to hybridize population-based metaheuristics (PBM) and particle filters (PF). The aim of this method is to naturally devise to effective hybrid algorithms to solve dynamic optimization problems by exploiting the benefits of both approaches. Derived algorithms cleverly combine PF and PBM frameworks. As particular examples, two different effective algorithms, named Path Relinking Particle Filter (PRPF) and Scatter Search Particle Filter (SSPF) are respectively derived from the proposed hybridization method. Finally, efficient applications of these instantiated algorithms to different dynamic problems are also presented.
Year
DOI
Venue
2005
10.1109/ICHIS.2005.62
HIS
Keywords
Field
DocType
dynamic optimization problem,pbm framework,powerful method,proposed hybridization method,dynamic optimization problems,real-world optimization problem,optimization problem,population-based metaheuristics,hybridizing particle filters,different dynamic problem,particle filter,non-gaussian discrete-time dynamic model,general method
Population,Mathematical optimization,Nonlinear system,Particle filter,Dynamic models,Engineering,Dynamic problem,Optimization problem,Bayesian probability,Metaheuristic
Conference
ISBN
Citations 
PageRank 
0-7695-2457-5
2
0.40
References 
Authors
11
2
Name
Order
Citations
PageRank
Juan Jose Pantrigo1191.56
Angel Sanchez2845.73