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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Published in:International journal of swarm intelligence research Vol. 4; no. 1; pp. 35 - 61
Main Author: Folly, Komla A
Format: Journal Article
Language:English
Published: Hershey IGI Global 01.01.2013
Subjects:
Algorithms
Controllers
Disturbances
Evolutionary algorithms
Genetic algorithms
Learning
Machine learning
Optimization
Searching
Swarm intelligence
ISSN:1947-9263, 1947-9271
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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.
AbstractList 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.
Author Folly, Komla A
AuthorAffiliation Department of Electrical Engineering, University of Cape Town, Cape Town, South Africa
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  organization: Department of Electrical Engineering, University of Cape Town, Cape Town, South Africa
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Snippet Population-Based Incremental Learning (PBIL) is a relatively new class of Evolutionary Algorithms (EA) that has been recently applied to a range of...
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SubjectTerms Algorithms
Controllers
Disturbances
Evolutionary algorithms
Genetic algorithms
Learning
Machine learning
Optimization
Searching
Swarm intelligence
Title An Improved Population-Based Incremental Learning Algorithm
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