Abstract
Ant colony optimization (ACO) is an intelligent bionic algorithm which simulates the foraging behavior of ant colony. The conventional ACOs mainly deal with the static optimization problems. In other words, the environment of problem maintains invariant. Actually, the most problems in reality are dynamic, namely, the changing environments. The ACO can use its robustness and self-adaptability to resolve dynamic problems properly. In this chapter, the ACO with neighborhood search is introduced to address dynamic traveling salesman problem and the ACO with improved K-means clustering algorithm, which uses three immigrants schemes including random immigrants, elitism-based immigrants and memory-based immigrants, is used for dynamic location routing problem. Several conventional ACOs and other heuristic algorithms are utilized to compare with new ACOs in the corresponding dynamic problems. The comparative experiments demonstrate two novel ACOs are effective and efficient for respective dynamic optimization problems.
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