کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
385547 | 660868 | 2011 | 6 صفحه PDF | دانلود رایگان |

Research on optimization in continuous domains gains much of focus in swarm computation recently. A hybrid ant colony optimization approach which combines with the continuous population-based incremental learning and the differential evolution for continuous domains is proposed in this paper. It utilizes the ant population distribution and combines the continuous population-based incremental learning to dynamically generate the Gaussian probability density functions during evolution. To alleviate the less diversity problem in traditional population-based ant colony algorithms, differential evolution is employed to calculate Gaussian mean values for the next generation in the proposed method. Experimental results on a large set of test functions show that the new approach is promising and performs better than most of the state-of-the-art ACO algorithms do in continuous domains.
► We combine continuous population-based incremental learning with the differential evolution for continuous domains.
► Dynamically generate the Gaussian probability density functions during evolution.
► Use differential evolution to calculate the Gaussian mean values for the next generation during evolution.
Journal: Expert Systems with Applications - Volume 38, Issue 9, September 2011, Pages 11072–11077