A Hybrid Genetic Algorithm for Partitioning of Data Model in Distribution Management Systems

In this paper, we propose a Hybrid Genetic Algorithm for data model partitioning of power distribution network. Analytical functions are the core of Distribution Management Systems (DMSs). Efficient calculation of the functions is of the utmost importance for the DMS users; the necessary preconditions for the efficient calculation are optimal load balancing of processors and data model partitioning among processors. The proposed algorithm is applied to different real models of power distribution systems. It obtains better results than classical evolutionary algorithms (Genetic Algorithm and Particle Swarm Optimization). The Hybrid Genetic Algorithm also achieves better results than multilevel algorithm (METIS) in cases of small graphs.