Memory-Bounded Ant Colony Optimization With Dynamic Programming and $A^{\ast}$ Local Search for Generator Planning

Swarm-inspired optimization has become very popular in recent years. Ant colony optimization (ACO) is successfully applied in the traveling salesman problem. Performance of the basic ACO for small problems with moderate dimensions and searching space is satisfactory. As the searching space grows exponentially in large-scale power systems generator planning, the basic ACO is not applicable for the vast size of the pheromone matrix of the ACO in practical-time and physical computer-memory limits. However, memory-bounded methods prune the least-promising nodes to fit the system in the computer memory. Therefore, the authors propose a memory-bounded version of ant colony optimization (MACO) with selected dynamic programming search in this paper for the scalable generator planning problem. This MACO solves the limitation of computer memory, and does not permit the system to grow beyond a bound on memory. In the memory-bounded ACO implementation, A* heuristic is introduced to increase local searching ability and the authors propose probabilistic nearest neighbor approach to estimate pheromone intensity for the forgotten value. Finally, the benchmark data and methods are used to show the effectiveness of the proposed method.