计算机技术与发展2012，Vol.22Issue(6)：39-44,6.
一类新型改进的广义蚁群优化算法
A New Improved Generalized Ant Colony Optimization Algorithm
摘要
Abstract
A new improved generalized ant colony optimization algorithm (IGACO) is proposed in this paper. The selected probability functions are generalized from strictly increasing continuous functions to bounded functions, which gives a more general form of expression for the probability of selecting the next node. An important theorem is proved for describing the convergence of IGACO algorithm, I.e. For a sufficiently large number of algorithm iterations, the probability of finding the globally optimal solution at least once tends to 1. A principle of pheromone asymptotic balance is proposed. In the pheromone update rule,the residual rate function of pheromone and the global increasing function of pheromone are presented. Prove that the residual pheromone tends to a positive number on the edges that are globally optimal solution,and tends to 0 on the edges that are not globally optimal solution. Finally,the computational simulation shows that,compared with traditional ant colony optimization algorithm,the IGACO algorithm has good performance both on globally optimal solution and convergent speed.关键词
人工智能/蚁群优化算法/收敛性/信息素更新规则Key words
artificial intelligence/ant colony optimization algorithm/convergence/pheromone update rule分类
信息技术与安全科学引用本文复制引用
张代远..一类新型改进的广义蚁群优化算法[J].计算机技术与发展,2012,22(6):39-44,6.基金项目
江苏高校优势学科建设工程资助项目(yx002001) （yx002001）
