| Summary: | 碩士 === 國立中山大學 === 資訊工程學系研究所 === 99 === This thesis presents a novel algorithm called ant colony optimization with dual pheromone tables
(ACODPT) for improving the quality of ant colony optimization (ACO). The proposed
algorithm works by adding a so-called “negative” pheromone table to ACO to avoid the problem
of ACO easily falling into local optima. By using the “negative” pheromone table to
eliminate the most impossible path to search for the new solution, the probability of selecting
the remaining paths is increased, and so is the quality. To evaluate the performance of the proposed
algorithm, ACODPT is compared with several state-of-the-art algorithms in solving the
clustering problem. The experimental results show that the proposed algorithm can eventually
prevent ACO from falling into local optima in the early iterations, thus providing a better result
than the other algorithms in many cases.
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