P-Complete Approximation Problems
Journal of the ACM (JACM)
ACO algorithms for the quadratic assignment problem
New ideas in optimization
Future Generation Computer Systems
Real-World Applications of Evolutionary Computing, EvoWorkshops 2000: EvoIASP, EvoSCONDI, EvoTel, EvoSTIM, EvoROB, and EvoFlight
Ant Colony Optimization
Stochastic Local Search: Foundations & Applications
Stochastic Local Search: Foundations & Applications
An external partial permutations memory for ant colony optimization
EvoCOP'05 Proceedings of the 5th European conference on Evolutionary Computation in Combinatorial Optimization
VERY STRONGLY CONSTRAINED PROBLEMS: AN ANT COLONY OPTIMIZATION APPROACH
Cybernetics and Systems
Iterated Greedy Algorithms for a Real-World Cyclic Train Scheduling Problem
HM '08 Proceedings of the 5th International Workshop on Hybrid Metaheuristics
Incorporating Tabu Search Principles into ACO Algorithms
HM '09 Proceedings of the 6th International Workshop on Hybrid Metaheuristics
Cunning ant system for quadratic assignment problem with local search and parallelization
PReMI'07 Proceedings of the 2nd international conference on Pattern recognition and machine intelligence
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
An ant colony optimization based algorithm for identifying gene regulatory elements
Computers in Biology and Medicine
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Ant colony optimization (ACO) algorithms construct solutions each time starting from scratch, that is, from an empty solution. Differently from ACO algorithms, iterated greedy, another constructive stochastic local search method, starts the solution construction from partial solutions. In this paper, we examine the performance of a variation of $\mathcal{MAX}$-$\mathcal{MIN}$ Ant System, one of the most successful ACO algorithms, that exploits this idea central to iterated greedy algorithms. We consider the quadratic assignment problem 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. The usage of external memory resulted in ACO variants, where partial solutions are used to seed the solution construction. Contrary to previously reported results on external memory usage, our computational results are more pessimistic in the sense that starting the solution construction from partial solutions does not necessarily lead to improved performance when compared to state-of-the-art ACO algorithms.