Title :
Combination of Genetic Algorithm and Ant Colony Algorithm for Distribution Network Planning
Author :
Dong, Yong-Feng ; Gu, Jun-hua ; Li, Na-Na ; Hou, Xiang-Dan ; Wei-Li Yan
Author_Institution :
Hebei Univ. of Technol., Tianjin
Abstract :
Ant colony algorithm is one kind of new heuristic biological modelling method which has the ability of parallel processing and global searching, but its convergence speed is slow because of poor pheromone on the early path. In this paper, discuss a new algorithm which combines genetic algorithm and Ant colony algorithm. Genetic algorithm is added to ant colony algorithm´s every generation in the proposed algorithm. Making use of genetic algorithm´s advantage of whole quick convergence, ant colony algorithm´s convergence speed is quickened. Genetic algorithm´s mutation mechanism improves the ability of ant colony algorithm to avoid being trapped in a local optimal. The simulation shows that the new algorithm is effective in solving distribution network planning problem.
Keywords :
distribution networks; genetic algorithms; power system planning; ant colony algorithm; distribution network planning; genetic algorithm mutation; Ant colony optimization; Biological system modeling; Convergence; Cost function; Cybernetics; Genetic algorithms; Investments; Machine learning; Machine learning algorithms; Path planning; Ant colony algorithm; Combinatorial optimization; Distribution network planning; Genetic algorithm;
Conference_Titel :
Machine Learning and Cybernetics, 2007 International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-0973-0
Electronic_ISBN :
978-1-4244-0973-0
DOI :
10.1109/ICMLC.2007.4370288