Using Genetic Algorithms to Optimize ACS-TSP
ANTS '02 Proceedings of the Third International Workshop on Ant Algorithms
Ant Colony Optimization
Settings of Algorithm Parameters in Ant Colony Algorithm
ICCSIT '08 Proceedings of the 2008 International Conference on Computer Science and Information Technology
ICNC '08 Proceedings of the 2008 Fourth International Conference on Natural Computation - Volume 07
Ant Colony Optimization Algorithm Based on Adaptive Weight and Volatility Parameters
IITA '08 Proceedings of the 2008 Second International Symposium on Intelligent Information Technology Application - Volume 02
Adaptive particle swarm optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
The influence of run-time limits on choosing ant system parameters
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Parametric study for an ant algorithm applied to water distribution system optimization
IEEE Transactions on Evolutionary Computation
Clustering-Based Adaptive Crossover and Mutation Probabilities for Genetic Algorithms
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Adaptive differential evolution with optimization state estimation
Proceedings of the 14th annual conference on Genetic and evolutionary computation
| Hi-index | 0.00 |
Parameters values have significant effects on the performance of the ant colony system (ACS) algorithm. However, it is a difficult task to choose proper parameters values for achieving the best performance of the algorithm. That is because the best parameters values are not only dependent on specific problems, but also related to the optimization states during the search process. This paper proposes a novel adaptive parameters control scheme for ACS and develops an adaptive ACS (AACS) algorithm. Different from the existing parameters control schemes, the parameters values in AACS are adaptively controlled according to the current optimization state, which is estimated based on measuring the pheromone trails distribution. The proposed AACS algorithm is applied to solve a series of benchmark traveling salesman problems (TSPs). The resulting solution quality and the convergence rate of AACS are favorably compared with the results by the ACS using fixed parameters values and two existing adaptive parameters control methods. Experimental results show that our proposed method is effective and competitive.