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.KeywordsAdaptive learning ratelow frequency oscillationspopulationbased incremental learningpower system stabilizer

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