Economic load distribution based on genetic-tabu hybrid algorithm

This paper presents a genetic-tabu search hybrid algorithm for solving power system economic load distribution (ELD). Genetic algorithm (GA) is faster in finding the high performance region but displays difficulties in performing local search for complex function. It leads to a poor fine tuning of the final solution. Tabu search (TS) is based on the neighborhood search of the hill climbing method. It allows to escaping from a local minimum and finds out better solutions. The proposed method presents a new strategy to combine genetic algorithm and tabu search. First, genetic algorithm is not stopped to search in the global solution space until premature happens. The outcome of genetic algorithm, which is promising solutions, is used as the initial population of TS, so tabu search can get good results. Effectiveness of the method was compared with many conventional methods. Results show that the proposed method has better convergence characteristics and robustness.