PSO is a powerful evolutionary algorithm used for finding global solution to a multidimensional problem. Particles in PSO tend to re-explore already visited bad solution regions of search space because they do not learn as a whole. This is avoided by restricting particles into promising regions through probabilistic modeling of the archive of best solutions. This paper presents hybrids of estimation of distribution algorithm and two PSO variants. These algorithms are tested on benchmark functions having high dimensionalities. Results indicate that the methods strengthen the global optimization abilities of PSO and therefore, serve as attractive choices to determine solutions to optimization problems in areas including sensor networks.

Meeting Name

International Conference on Intelligent Sensors, Sensor Networks and Information, 2007. ISSNIP 2007


Electrical and Computer Engineering

Keywords and Phrases

Estimation Theory; Evolutionary Computation; Particle Swarm Optimisation; Probability; Search Problems

Document Type

Article - Conference proceedings

Document Version

Final Version

File Type





© 2007 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 Dec 2007