The Edge-set Encoding in Evolutionary Algorithms for Power Distribution Network Planning Problem Part I: Single-objective Optimization Planning
Francisco Rivas-Davalos, Malcolm R. Irving · 2006
In this paper we propose representing solutions in evolutionary algorithms for power distribution network expansion planning problems using the edge-set encoding technique, and we describe recombination and mutation operators for this representation. We demonstrate the usefulness of this encoding technique in a genetic algorithm designed to deal with the planning problem formulated as a single-objective optimization problem: to find the best location and size of substations and lines to minimize a cost function of the network. The algorithm was tested on a real power distribution network and the results were compared with the results from other heuristic methods. We concluded that the edge-set encoding and its genetic operators in evolutionary algorithms for power distribution network expansion planning offer strong locality and heritability, and computational efficiency. In the companion paper, the edge-set encoding technique is tested on a multi-objective power distribution network planning problem