Abstract
Population-Based Incremental Learning (PBIL) is a combination of Genetic Algorithm with competitive learning derived from Artificial Neural Network. It has recently received increasing attention due to its effectiveness, easy implementation and robustness. Despite these strengths, it has been reported recently that PBIL suffers from issues of loss of diversity in the population. To deal with the issue of premature convergence, we propose in this paper a parallel PBIL based on multi-population. In parallel PBIL, two populations are used where both probability vectors (PVs) are initialized to 0.5. The approach is used to design a power system controller for damping low-frequency oscillations. To show the effectiveness of the approach, simulations results are compared with the results obtained using standard PBIL and another diversity increasing PBIL called herein as PBIL with Adapting learning rate (APBIL). It is shown that Parallel PBIL approach performs better than the standard PBIL and is as effective as APBIL.
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Folly, K.A. (2014). Comparison of Multi-population PBIL and Adaptive Learning Rate PBIL in Designing Power System Controller. In: Tan, Y., Shi, Y., Coello, C.A.C. (eds) Advances in Swarm Intelligence. ICSI 2014. Lecture Notes in Computer Science, vol 8795. Springer, Cham. https://doi.org/10.1007/978-3-319-11897-0_16
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DOI: https://doi.org/10.1007/978-3-319-11897-0_16
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