Title :
An Improved Ant Colony Optimization and Its Applications in Flow-Shop Problems
Author :
Song, Xuemei ; Wang, Kun ; Xiao, Yang
Author_Institution :
Comput. & Autocontrol Dept., Hebei Polytech. Univ., Tangshan, China
Abstract :
Ant colony optimization (ACO) is easily relapsed into local optimization and stagnation. In order to ameliorate this problem existed in ACO, several new improvements are proposed and evaluated. Such as, stochastic search strategy and pheromone mutation were inducted. Then an improved ant colony optimization with pheromone mutation (PMACO) was put forward. It was tested by a set of benchmark travelling salesman problems from the travelling salesman problem library and some flow-shop problems. The results of the examples show that it can not easily run into the local optimum and can converge at the global optimum. It performs better than the other algorithms such as genetic algorithm in solving flowshop problems.
Keywords :
flow shop scheduling; genetic algorithms; travelling salesman problems; benchmark travelling salesman problems; flow-shop problems; genetic algorithm; improved ant colony optimization with pheromone mutation; local optimization; local stagnation; stochastic search strategy; travelling salesman problem library; Ant colony optimization; Application software; Benchmark testing; Cities and towns; Genetic algorithms; Genetic mutations; Libraries; Optimization methods; Stochastic processes; Traveling salesman problems;
Conference_Titel :
Computational Intelligence and Software Engineering, 2009. CiSE 2009. International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-4507-3
Electronic_ISBN :
978-1-4244-4507-3
DOI :
10.1109/CISE.2009.5363536
