New approach with a genetic algorithm framework to multi-objective generation dispatch problems

SUMMARY This paper presents a new improved genetic multi-objective optimization algorithm for generation dispatch problems aiming to minimize two objectives—cost and emission. The Improved Genetic Algorithm (IGA) equipped with an improved evolutionary direction operator and a migration operation can efficiently search and actively explore solutions. The Multiplier Updating technique is introduced to avoid deforming the augmented Lagrange function and reduce the difficulty in solution searching. To handle the multi-objective problem, the "constraint technique is employed. The proposed approach integrates the "-constraint technique, IGA, and the Multiplier Updating technique. The proposed method has the merits of automatically adjusting the randomly given penalty to a proper value and requiring only a small-size population. Extensive simulations using the proposed method are carried out on variously sized systems, and the results are compared with those obtained using other methods. Numerical results indicate that the proposed approach is superior to other methods in solution quality and computational burden. Copyright # 2005 John Wiley & Sons, Ltd.