A Seeded Memetic Algorithm for Large Unit Commitment Problems

The paper shows that the use of a memetic algorithm (MA), a genetic algorithm (GA) combined with local search, synergistically combined with Lagrangian relaxation is effective and efficient for solving large unit commitment problems in electric power systems. It is shown that standard implementations of GA or MA are not competitive with the traditional methods of dynamic programming (DP) and Lagrangian relaxation (LR). However, an MA seeded with LR proves to be superior to all alternatives on large problems. Eight problems from the literature and a new large, randomly generated problem are used to compare the performance of the proposed seeded MA with GA, MA, DP and LR. Compared with previously published results, this hybrid approach solves the larger problems better and uses less computational time.