<![CDATA[Ant colony algorithm, a heuristic simulated algorithm, provides better solutions for non-convex, non-linear and discontinuous optimization problems. For ant colony algorithm, it is frequently to be trapped into local optimum, which might lead to stagnation. This article presents the city-select strategy, local pheromone update strategy, optimum solution prediction strategy and local optimization strategy to optimize ant colony algorithm, provides ant colony algorithm based on local pheromone update, also inspects and verifies it by TSP problems. The results of the numeric experiments suggest that on some TSP problems, the optimized ant colony algorithm acquired more satisfied solutions than all that we have already known. DOI : http://dx.doi.org/10.11591/telkomnika.v12i2.4211 ]]>
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