An Improved Population-Based Incremental Learning Algorithm

Komla A. Folly
Additional contact information
Komla A. Folly: Department of Electrical Engineering, University of Cape Town, Cape Town, South Africa

International Journal of Swarm Intelligence Research (IJSIR), 2013, vol. 4, issue 1, 35-61

Abstract: Population-Based Incremental Learning (PBIL) is a relatively new class of Evolutionary Algorithms (EA) that has been recently applied to a range of optimization problems in engineering with promising results. PBIL combines aspects of Genetic Algorithm with competitive learning. The learning rate in the standard PBIL is generally fixed which makes it difficult for the algorithm to explore the search space effectively. In this paper, a PBIL with adapting learning rate is proposed. The Adaptive PBIL (APBIL) is able to thoroughly explore the search space at the start of the run and maintain the diversity consistently during the run longer than the standard PBIL. The proposed algorithm is validated by applying it to power system controller parameters optimization problem. Simulation results show that the Adaptive PBIL based controller performs better than the standard PBIL based controller, in particular under small disturbance.

Date: 2013
References: Add references at CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 4018/jsir.2013010102 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jsir00:v:4:y:2013:i:1:p:35-61

Access Statistics for this article

International Journal of Swarm Intelligence Research (IJSIR) is currently edited by Yuhui Shi

More articles in International Journal of Swarm Intelligence Research (IJSIR) from IGI Global
Bibliographic data for series maintained by Journal Editor ().