Future Generation Computer Systems
Using Experimental Design to Find Effective Parameter Settings for Heuristics
Journal of Heuristics
Boosting ACO with a Preprocessing Step
Proceedings of the Applications of Evolutionary Computing on EvoWorkshops 2002: EvoCOP, EvoIASP, EvoSTIM/EvoPLAN
A Racing Algorithm for Configuring Metaheuristics
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Using Genetic Algorithms to Optimize ACS-TSP
ANTS '02 Proceedings of the Third International Workshop on Ant Algorithms
Ant Colony Optimization
Fine-Tuning of Algorithms Using Fractional Experimental Designs and Local Search
Operations Research
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 Optimization with Castes
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Improved Lower Limits for Pheromone Trails in Ant Colony Optimization
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
On the Explorative Behavior of MAX---MIN Ant System
SLS '09 Proceedings of the Second International Workshop on Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics
WiCOM'09 Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing
SamACO: variable sampling ant colony optimization algorithm for continuous optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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The impact of the values of the most meaningful parameters on the behavior of $\cal M\!AX\!$–$\cal MI\!N\!$ Ant System is analyzed. Namely, we take into account the number of ants, the evaporation rate of the pheromone, and the exponent values of the pheromone trail and of the heuristic measure in the random proportional rule. We propose an analytic approach to examining their impact on the speed of convergence of the algorithm. Some computational experiments are reported to show the practical relevance of the theoretical results.