Abstract
A novel approach for the use of external memory in ant colony optimization strategy for solving descriptor selection problem in quantitative structure-activity/property relationship studies is described. In this approach, several ant colony system algorithms are run to build an external memory containing a number of elite ants. In the next step, all of the elite ants in the external memory are allowed to update the pheromones. Then the external memory is emptied and the updated pheromones are used again, by several ant colony system algorithms to build a new external memory. These steps are iteratively run for certain number of iterations. At the end, the memory will be containing several top solutions to the problem. The proposed approach was applied to solving variable selection problem in quantitative structure-activity/property relationship studies of rate constants of o-methylation of 36 phenol derivatives and activities of 31 antifilarial antimycin compounds, for which the obtained results revealed that both the speed and the solution quality are improved compared to conventional ant colony system algorithms.
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