摘要
将社会演化算法和蚁群算法相结合,以蚁群算法作为认知主体的推理过程,再以范式的学习和更新方式获得最优解,提出一种求解TSP问题的社会演化算法。最后通过两个算例实验仿真与TSP已知最优解进行对比分析,结果表明,社会演化算法在种群规模较小,迭代次数较少的情况下也可获得TSP最优解。
In this paper,a method-social evolutionary programming which combines social evolutionary programming and ant colony optimization is given to solve TSP.Firstly ant colony optimization is used as cognitive agents cognitive learning,and then the global optimum is obtained by paradigm s learning and shift.Finally two examples are compared with the optimum that has been known,the result indicates that social evolutionary programming with fewer agents and less iterative times can also converge the optimum.
出处
《计算机工程与应用》
CSCD
北大核心
2009年第26期46-48,89,共4页
Computer Engineering and Applications
基金
国家自然科学基金No.60461001
广西自然科学基金No.0832082
国家民委科研基金No.08GX01
广西研究生教育创新计划资助项目(No.T32084)
广西民族大学科研项目启动基金~~
关键词
社会演化算法
蚁群算法
旅行商问题
social evolutionary programming
ant colony optimization
Travelling Salesman Problem（TSP）
