Transmission network expansion planning based on estimation of distribution algorithms

Abstract

Estimation of distribution algorithms (EDA) is a new kind of evolution algorithm, through the statistics of the information of selected individual in current group the probability estimation of the individual distribution in next generation is given, and the next generation of group is formed by random sampling. Here, the EDA is applied to solve the transmission network expansion planning problem, two models of transmission network expansion planning based on EDA, i.e., the population-based incremental learning: (PBIL) and factorized distribution algorithm (EDA), are put forward. The influence of several strategies, such as weighted estimation, random size of parents set selection, readjusting of the conditional probability sequence, stochastic mutation and elitist reserved on the algorithm is analyzed. Simulation results show that the used EDA is reliable and effective for solving the transmission network expansion planning problems.