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The Fusion Algorithm of Genetic and Ant Colony and Its Application

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2 Author(s)
Zhou Shenpei ; Sch. of Autom., Wuhan Univ. of Technol., Wuhan, China ; Yan Xinping

Genetic algorithm (GA) has strong adaptability, robustness and the quick global searching ability. It has such disadvantages as premature convergence, low convergence speed and so on. Ant colony algorithm (ACO) converges on the optimization path through pheromone accumulation and renewal. It has the ability of parallel processing and global searching and the characteristic of positive feedback. But the convergence speed of ACO is lower at the beginning for there is only little pheromone difference on the path at that time. The fusion algorithm of genetic and ant colony algorithm adopts genetic algorithm to give pheromone to distribute. And then it makes use of ant colony algorithm to give the precision of the solution. It develops enough advantage of the two algorithms. The comparative analysis on optimal performance of three algorithms is made by using the Camel function. Finally, the algorithm is used for the optimized the signal cycle length and green time by considering the constraint of automotive exhaust emission. The performance index function for optimization is defined to improve traffic quality and reduce emission at intersections. The simulation results show that very nice effects are obtained.

Published in:

Natural Computation, 2009. ICNC '09. Fifth International Conference on  (Volume:4 )

Date of Conference:

14-16 Aug. 2009