New insertion and postoptimization procedures for the traveling salesman problem
Operations Research
A tabu search heuristic for the vehicle routing problem
Management Science
Empirical methods for artificial intelligence
Empirical methods for artificial intelligence
ACO algorithms for the quadratic assignment problem
New ideas in optimization
Future Generation Computer Systems
Heuristics for Large Constrained Vehicle Routing Problems
Journal of Heuristics
INFORMS Journal on Computing
Ant Colony Optimization
Stochastic Local Search: Foundations & Applications
Stochastic Local Search: Foundations & Applications
A review of metrics on permutations for search landscape analysis
Computers and Operations Research
Rigorous Analyses for the Combination of Ant Colony Optimization and Local Search
ANTS '08 Proceedings of the 6th international conference on Ant Colony Optimization and Swarm Intelligence
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Ant colony optimization algorithms are currently among the best performing algorithms for the quadratic assignment problem. These algorithms contain two main search procedures: solution construction by artificial ants and local search to improve the solutions constructed by the ants. Incremental local search is an approach that consists in re-optimizing partial solutions by a local search algorithm at regular intervals while constructing a complete solution. In this paper, we investigate the impact of adopting incremental local search in ant colony optimization to solve the quadratic assignment problem. Notwithstanding the promising results of incremental local search reported in the literature in a different context, the computational results of our new ACO algorithm are rather negative. We provide an empirical analysis that explains this failure.