Optimization of large electric power distribution using a parallel genetic algorithm with dandelion strategy

The study of electrical distribution of primary networks design is oriented to reduce the construction costs and the energy losses by transmission. The topology for the implementation of distribution networks may vary according to the geographical characteristics of the final users and requires specialized optimization solutions with metaheuristics to improve the energy performance of the electrical power systems. A parallel genetic algorithm (PGA) is proposed to optimize a tree-based topology for large-scale electric power distribution networks. The proposed PGA uses the dandelion code, which allows obtaining tree-feasible solutions within each iteration of the PGA. This cannot be achieved with other metaheuristic approaches directly. Eight cores are used simultaneously. We achieve a 22.05% improvement when compared to the tree-feasible solutions obtained with its sequential version. Moreover, the computational time required by the PGA is on average 23 times lower than the sequential version. Finally, we find feasible solutions for instances of the problem with up to 50,000 nodes.