Future Generation Computer Systems
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Ant Colony Optimization
On MAX - MIN ant system's parameters
ANTS'06 Proceedings of the 5th international conference on Ant Colony Optimization and Swarm Intelligence
Improved Robustness through Population Variance in Ant Colony Optimization
SLS '09 Proceedings of the Second International Workshop on Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics
Ant colony system with selective pheromone memory for TSP
ICCCI'12 Proceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part II
| Hi-index | 0.00 |
Ant Colony Optimization algorithms were inspired by the foraging behavior of ants that accumulate pheromone trails on the shortest paths to food. Some ACO algorithms employ pheromone trail limits to improve exploration and avoid stagnation by ensuring a non-zero probability of selection for all trails. The MAX-MIN Ant System (MMAS) sets explicit pheromone trail limits while the Ant Colony System (ACS) has implicit pheromone trail limits. Stagnation still occurs in both algorithms with the recommended pheromone trail limits as the relative importance of the pheromone trails increases (茂戮驴 1). Improved estimates of the lower pheromone trail limit (茂戮驴min) for both algorithms help avoid stagnation and improve performance for 茂戮驴 1. The improved estimates suggest a general rule to avoid stagnation for stochastic algorithms with explicit or implicit limits on exponential values used in proportional selection.