Multi-Objective Power Network Planning Based on Improved Pareto Ant Colony Algorithm

As the present power system planning can hardly take economy and reliability into account, an improved Pareto ant colony algorithm (IPACA) is proposed in this paper. An improved quick sort method is applied to construct Pareto optimal solution set, thereby the slow-chain is shorten and the time complexity is reduced. Clustering analysis has been used to improve the diversity and distributivity of the Pareto optimal solution set. Global convergence rate of the algorithm is expedited, as the control parameter in the local and global pheromone update is vary with the iteration. The global search ability is enhanced by dynamic self-adapting adjust mechanism of the evaporation coefficient. The proposed algorithm is tested with 18-bus system and results show that the find Pareto optimal solution of it is more than the basic PACA, and distribution of the Pareto front is well-proportioned.