An improved TS algorithm for loss-minimum reconfiguration in large-scale distribution systems

This paper presents an improved tabu search (ITS) algorithm for loss-minimization reconfiguration in large-scale distribution systems. TS algorithm is an efficient meta-heuristic searching algorithm. It has advantages of both high local search efficiency of hill-climbing method and global search ability of intelligent algorithm. But tabu lengths and candidate neighborhood are two key parameters affecting searching performance of TS algorithm, and these two parameters are hard to be effectively determined in advance. In ITS algorithm, mutation operation, a main operator used in genetic algorithm, is introduced to weaken the dependence of global search ability on tabu length. In addition, the candidate neighborhood, which only contains several optimal switch exchanges in each tie switch associated loop network, is designed to improve local search efficiency and to save a large amount of computing time. The proposed ITS algorithm is applied to the sample system and numerical results well demonstrate the validity and effectiveness of the proposed ITS algorithm.