An Improved Population-Based Incremental Learning Algorithm

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...

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Veröffentlicht in:International journal of swarm intelligence research Jg. 4; H. 1; S. 35 - 61
1. Verfasser: Folly, Komla A
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Hershey IGI Global 01.01.2013
Schlagworte:
Algorithms
Controllers
Disturbances
Evolutionary algorithms
Genetic algorithms
Learning
Machine learning
Optimization
Searching
Swarm intelligence
ISSN:1947-9263, 1947-9271
Online-Zugang:Volltext
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Zusammenfassung: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.
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ISSN:1947-9263
1947-9271
DOI:10.4018/jsir.2013010102