  The power system planning task is a combinatorial optimization problem. The
objective function minimizes the economic costs subject to a set of technical
and operational constraints. Meta-heuristics are often used as optimization
strategies to find solutions to this problem by combining switching, line
reinforcement or new line measures. Common heuristics are GA, PSO, HC, ILS or
newer methods such as GWO or FWA. In this paper, we compare these algorithms
within the same framework. We test each algorithm on 8 different test grids
ranging from 73 to 9421 buses. For each grid and algorithm, we start 50 runs
with a maximum run time of 1 hour. The results show that the performance of an
algorithm depends on the initial grid state, grid size and amount of measures.
The ILS method is very robust in most cases. In the larger test grids, more
exploratory heuristics, e.g., GA and PSO, find solutions in shorter run times.
