Future Generation Computer Systems
Swarm intelligence
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Ant Colony Optimization
Enhancing Stochastic Search Performance by Value-Biased Randomization of Heuristics
Journal of Heuristics
Non-wrapping order crossover: an order preserving crossover operator that respects absolute position
Proceedings of the 8th annual conference on Genetic and evolutionary computation
An ant colony optimization for single-machine tardiness scheduling with sequence-dependent setups
Computers and Operations Research
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Off-line vs. on-line tuning: a study on MAX–MIN ant system for the TSP
ANTS'10 Proceedings of the 7th international conference on Swarm intelligence
Engineering Applications of Artificial Intelligence
International Journal of Hybrid Intelligent Systems
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We propose a self-adaptive Ant Colony Optimization (AD-ACO) approach that exploits a parameter adaptation mechanism to reduce the requirement of a preliminary parameter tuning. The proposed AD-ACO is based on an ACO algorithm adopting a pheromone model with a new global pheromone update mechanism. We applied this algorithm to the single machine total weighted tardiness scheduling problem with sequence-dependent setup times and we executed an experimental campaign on a benchmark available in literature. Results, compared with the ones produced by the ACO algorithm without adaptation mechanism and with those obtained by recently proposed metaheuristic algorithms for the same problem, highlight the quality of the proposed approach.