P-Complete Approximation Problems
Journal of the ACM (JACM)
ACO algorithms for the quadratic assignment problem
New ideas in optimization
Future Generation Computer Systems
Tabu Search
Ant Colony Optimization
cAS: ant colony optimization with cunning ants
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
An external partial permutations memory for ant colony optimization
EvoCOP'05 Proceedings of the 5th European conference on Evolutionary Computation in Combinatorial Optimization
Iterated ants: an experimental study for the quadratic assignment problem
ANTS'06 Proceedings of the 5th international conference on Ant Colony Optimization and Swarm Intelligence
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
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ACO algorithms iteratively build solutions to an optimization problem. The solution construction process is guided by pheromone trails which represents a mechanism of adaptation that allows to bias the sampling of new solutions toward promising regions of the search space. Additionally, the bias of the search is influenced by problem dependent heuristic information. In this work we describe an ACO algorithm that incorporates principles of Tabu Search (TS) for the solution construction process. These concepts specifically address the way that TS uses the history of the search to avoid visiting solutions already analyzed. We consider the Quadratic Assignment Problem (QAP) as a case-study, since this problem was also tackled in a closely related research to ours, the one on the usage of external memory in ACO algorithms. The performance of the proposed algorithm is assessed by considering a well-known set of instances of QAP.