A parallel ant colony optimization algorithm based on visual and memory model

作者：Chen Xin^*; Zhou Yunbin; Guo He; Cheng Tong

来源：Journal of Computational Information Systems, 2012, 8(15): 6429-6436.

• Ant colony algorithms
• Convergence rates
• Learning abilities
• Learning models
• Learning Theory
• Local optimal
• Memory models
• Parallel ant colony optimization
• Parallel improvement
• Parallel models
• Searching ability
• Serial algorithms
• Solution quality
• Visual feedback
• Visual model
• Visual perception

An ant colony algorithm model with visual feedback, behavior memory and learning ability is proposed in this paper. Ants can not only perceive the distribution of the target cities around through visual model to improve its searching quality, but also extract experience from the local optimal path to guide the searching activities through memory behaviors and learning models, which could be used to accelerate the convergence rate and to strengthen the searching ability. Then the parallel transformation of the new model are used in the GPU environment. Experiments show that the parallel algorithm under the new model has great improvement in solution quality and solving time compared to other parallel models. While compared with the serial algorithm of the same model, the parallel algorithm could make a good speedup.

• 出版日期2012