In recent years, the trend in solving optimization problems has been directed toward using heuristic algorithms such as neural networks, genetic and ant colony algorithms. The main reason for this trend can be attributed to the fact that these algorithms can be efficiently adjusted to the specific search space to which they are applied and consequently they can be used for many optimization problems of different nature. In this paper, the behavior of classical ACS algorithm in finding collapse load factor of two-dimensional frames is investigated closely. Time consuming and redundant parts that greatly affect the performance are removed leading to an accelerated ACS algorithm called variant one in this work. For some frames with certain combination of plastic moments and loadings the first variant does not lead to acceptable results. Therefore a few constraints intended to accelerate the variant one of ACS algorithm are eliminated and some provisions are added to bias the solution toward ant decision making strategy rather than problem dependent information. Consequently a new variant called variant two is proposed that can be used for a wider range of frames with of course more computational effort.