摘要
提出了一种新的求连续空间最优值的蚁群算法。结合遗传算法和蚁群算法各自的优点以及两种算法融合基础,提出了遗传算法融入到蚁群算法融合中的两种新策略,第一种策略是先利用遗传算法具有比较强的全局搜索能力,在大范围内寻找一组解,然后以此为基础,用蚁群算法快速寻找最优解X*best;另一种策略是利用遗传算法交叉操作产生蚁群算法中的新旅行路径,以此提高蚁群算法的全局搜索能力。用上述策略构造两个基于遗传算法的混合遗传算法。用测试函数Rosenbrock和测试函数Shubert验证了混合蚁群算法的正确性。
Propose a new Ant Colony System （ACS） for obtaining optimal value of continuous space.Comparing their advantages and disadvantages between Genetic Algorithm （GA） and Ant Colony System and analyzing their basic fusion condition,propose two new strategies of fusing GA into ACS：One is first using genetic algorithm to obtain some rough solutions to the problem and then obtaining the more precise solutions X^＊ best by ACS,the other is improving the ability of global search by using two tour paths in ACS to generate another two new tour paths like crossover operation of GA.Based on above new ideas,two new hybrid ant colony systems based on GA respectively called GA-HACS-I and GA-HACS-II are built in this paper.At last,verify the correction of GA-HACS-I and GA-HACS-II by test function Rosenbrock and test function Shubert.
出处
《计算机工程与应用》
CSCD
北大核心
2008年第16期42-45,134,共5页
Computer Engineering and Applications
基金
国家自然科学基金(the National Natural Science Foundation of China under Grant No.5027150)
高等院校博士学科点专项科研基金(the China Specialized Research Fund for the Doctoral Program of Higher Education under Grant No.20040533035)
关键词
遗传算法
混合蚁群算法
算法融合
连续空间优化
genetic algorithm
hybrid ant colony system
algorithm fusion
optimization of continuous space
