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 (CA) 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 (IS) 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 IS, 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.
thermal power plant economic load distribution genetic algorithm tabu search
YAO Jing FANG Yan-jun GUO Lin
School of Power and Mechanical Engineering, Wuhan University, Wuhan 430072, Hubci Province, China Fa School of Power and Mechanical Engineering, Wuhan University, Wuhan 430072, Hubci Province, China Faculty of Physics and Electronics, Hubci University, Wuhan 430062, Hubci Province, China
国际会议
武汉
英文
59-62
2011-08-20（万方平台首次上网日期，不代表论文的发表时间）