Unit commitment by Genetic Evolving Ant Colony Optimization

Ant Colony Optimization (ACO) is more suitable for combinatorial optimization problems. This paper proposes Genetic Evolving Ant Colony Optimization (EACO) method for solving unit commitment (UC) problem. The EACO employs Genetic Algorithm (GA) for finding optimal set of ACO parameters, while ACO solves the UC problem. Problem formulation takes into consideration the minimum up and down time constraints, start up cost, shut-down cost, spinning reserve, ramp rate constraints and generation limit constraints. The feasibility of the proposed approach is demonstrated for 4 and 10-unit systems. The test results are encouraging and are compared with those obtained by other methods.