摘要
针对蚁群算法加速收敛和早熟停滞现象的矛盾,借鉴免疫系统的自我调节机制来保持种群的多样性的能力,提出免疫-蚁群算法。该算法根据解的微观多样性、宏观多样性和弧的浓度指标动态调整路径选择概率和信息量更新策略。以数种对称和不对称TSP问题为例进行仿真实验。结果表明,该算法比一般蚁群算法具有更好的局部求精能力、收敛性和多样性,更适合于求解大规模的TSP问题。
To solve the contradictory between convergence speed and precocity and stagnation in ant colony algorithm, this paper references the ability of the self-regulation mechanism to maintain the population diversity on immune algorithm. The immune-ant colony algorithm is presented. According to micro-diversity, macro-diversity and the concentration of arc, this algroithm dynamically adjusts the selection probabilities of the paths and the trail information updating. Simulation experimental results on symmetric and asymmetric TSP show that the presented algorithm has much better intensification and diversification than that of classical ant colony algorithm and is more suitable for solving large scale TSP.
出处
《计算机工程》
CAS
CSCD
北大核心
2010年第24期156-157,160,共3页
Computer Engineering
基金
教育部科学技术研究基金资助重点项目(206073)
福建省自然科学基金资助重点项目(A0820002)
福建省自然科学基金资助项目(2009J01284)
福建省科技创新平台计划基金资助项目(2009J1007)
福建省教育厅基金资助项目(2007JB07024)
关键词
蚁群算法
免疫算法
多目标优化
旅行商问题
信息素
ant colony algorithm
immune algorithm
multi-objective optimization
traveling salesman problem
pheromone
