摘要
蚁群算法求解组合优化问题是当今智能优化算法的发展方向之一。通过对M.Dorigo提出的传统蚁群优化元启发模型改进,提出了多参数约束蚁群优化元启发式模型。该模型将所有优化约束条件映射为影响人工蚂蚁搜索行为的诱导素;模型中的人工蚂蚁智能行为简单,只根据信息素和诱导素在求解空间中进行搜索,而不进行复杂的运算;该模型减少了人工蚂蚁的求解搜索空间。并通过受时间、空间约束问题VRP(Vehicle Routing Problem)验证了本文提出模型算法较传统蚁群算法简单、收敛性快。
The combination optimization problem solving with the ant colony optimization approach is a new trend in artificial intelligence optimization. By a model put forward by M. Dorigo in sol nalyzing the deficiency of traditional ant colony optimization ving muhi-parameters constrained optimization problem, this optimization meta heuristic model. This model transforms the multiparameters constrained conditions into corresponding inducements which affect the behaviors of artificial ants; ants in this model search the solution only by pheromone and inducements; artificial ants in this strategy do not do any complicated computation, and this approach reduces the search space of artificial ants. Finally, this paper verified the simplicity and much fast convergence of the model by solving time, space constrained vehicle routing problem （VRP）.
出处
《山东科技大学学报（自然科学版）》
CAS
2008年第4期43-47,52,共6页
Journal of Shandong University of Science and Technology(Natural Science)
基金
上海市教委项目(CL200652):基于蚁群算法无线移动Web服务描述及发现算法研究
上海市教委项目(DKL709):基于动态蚁群算法无线传感器网络自适应路由协议的研究
关键词
蚁群算法
约束优化
VRP
TSP
元启发式
ant colony algorithm
conditional optimization
vehicle routing problem
traveling salesman problem
metaheuristic
