Title :
An improved ant colony algorithm for continuous space optimization
Author :
Yang, Liang ; Fu, Zheng-qi ; De Wang ; Li, He-long ; Xia, Jing-bo
Author_Institution :
Coll. of Sci., Huazhong Agric. Univ., Wuhan, China
Abstract :
Based on the mechanism of the improved ant colony algorithm, a novel method in continuous space optimization is developed. Four novel strategies are used in this new method: add random ants and elitist ants, improve the move strategy, alter the parameters dynamically, and modify the peak value in the pheromone distribution function. Simulation results show that the improved algorithm achieves faster convergence speed and better global optimization, while compared with the simulation results of original algorithm.
Keywords :
convergence; optimisation; ant colony algorithm; continuous space optimization; convergence speed; elitist ants; global optimization; random ants; Algorithm design and analysis; Convergence; Distribution functions; Heuristic algorithms; Machine learning algorithms; Optimization; Simulation; Ant colony algorithm; Continuous function optimization;
Conference_Titel :
Machine Learning and Cybernetics (ICMLC), 2010 International Conference on
Conference_Location :
Qingdao
Print_ISBN :
978-1-4244-6526-2
DOI :
10.1109/ICMLC.2010.5580957
