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Population-Based Incremental with Adaptive Learning Rate Strategy

  • Komla A. Folly
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7331)

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 longer than the standard PBIL. To show its effectiveness, the proposed algorithm is applied to the problem of optimizing the parameters of a power system controller. Simulation results show that APBIL based controller performs better than the standard PBIL based controller.

Keywords

Adaptive learning rate low frequency oscillations populationbased incremental learning power system stabilizer 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Komla A. Folly
    • 1
  1. 1.Department of Electrical EngineeringUniversity of Cape TownCape TownSouth Africa

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