Ant Colony Optimization Algorithm for Continuous Domains

摘要：

Ant colony optimization is one of the popular metaheuristics used for tackling optimization problems. In this paper, we present a novel idea on how ACO may be extended to continuous domains with the pheromone modeled by probability density functions instead of a table. We present a fully functional algorithm and evaluate the performance of our algorithm on a real-world problem of training neural networks for pattern classification .Evaluation results demonstrate that it is competitive, when compared to other algorithms.

关键词： Index Terms - Ant Colony Optimization (ACO) Continuous Optimization Problems (COPs) Neural Networks(NNs) Classification Error Percentage (CEP)

作者: Tingtang Ming Ruipeng Ding Li jun

作者单位: Network Information Center University of Henan Kaifeng, Henan Province, China

会议类型: 国际会议

会议名称: 2010 International Conference on Computer and Communication Technologies in Agriculture Engineering(计算机与通信技术在农业工程国际会议 CCTAE 2010)

会议地点: 成都

会议语种:英文

页码: 412-418

在线出版日期: 2010-06-12（万方平台首次上网日期，不代表论文的发表时间）