An Ant Colony Optimization Algorithm for Partitioning Graphs with Supply and Demand
This addresses optimization in electrical distribution systems, such as microgrid self-adequacy, but is incremental as it builds on existing methods.
The paper tackled the maximum partitioning of graphs with supply and demand problem, proposing an ant colony optimization algorithm combined with a correction procedure, which found optimal solutions in over 50% of instances and had an average relative error below 0.5%.
In this paper we focus on finding high quality solutions for the problem of maximum partitioning of graphs with supply and demand (MPGSD). There is a growing interest for the MPGSD due to its close connection to problems appearing in the field of electrical distribution systems, especially for the optimization of self-adequacy of interconnected microgrids. We propose an ant colony optimization algorithm for the problem. With the goal of further improving the algorithm we combine it with a previously developed correction procedure. In our computational experiments we evaluate the performance of the proposed algorithm on both trees and general graphs. The tests show that the method manages to find optimal solutions in more than 50% of the problem instances, and has an average relative error of less than 0.5% when compared to known optimal solutions.