Future Generation Computer Systems
Modeling the dynamics of ant colony optimization
Evolutionary Computation
Ant Colony Optimization
Stochastic Local Search: Foundations & Applications
Stochastic Local Search: Foundations & Applications
On the runtime analysis of the 1-ANT ACO algorithm
Proceedings of the 9th annual conference on Genetic and evolutionary computation
First steps to the runtime complexity analysis of ant colony optimization
Computers and Operations Research
Comparing variants of MMAS ACO algorithms on pseudo-boolean functions
SLS'07 Proceedings of the 2007 international conference on Engineering stochastic local search algorithms: designing, implementing and analyzing effective heuristics
ANTS'06 Proceedings of the 5th international conference on Ant Colony Optimization and Swarm Intelligence
Runtime analysis of a simple ant colony optimization algorithm
ISAAC'06 Proceedings of the 17th international conference on Algorithms and Computation
How Single Ant ACO Systems Optimize Pseudo-Boolean Functions
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Running Time Analysis of ACO Systems for Shortest Path Problems
SLS '09 Proceedings of the Second International Workshop on Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics
Using markov-chain mixing time estimates for the analysis of ant colony optimization
Proceedings of the 11th workshop proceedings on Foundations of genetic algorithms
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Variation in artificial immune systems: hypermutations with mutation potential
ICARIS'11 Proceedings of the 10th international conference on Artificial immune systems
Running time analysis of Ant Colony Optimization for shortest path problems
Journal of Discrete Algorithms
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Computational Biology and Chemistry
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Ant colony optimization (ACO) is a metaheuristic that produces good results for a wide range of combinatorial optimization problems. Often such successful applications use a combination of ACO and local search procedures that improve the solutions constructed by the ants. In this paper, we study this combination from a theoretical point of view and point out situations where introducing local search into an ACO algorithm enhances the optimization process significantly. On the other hand, we illustrate the drawback that such a combination might have by showing that this may prevent an ACO algorithm from obtaining optimal solutions.