摘要
针对遗传算法在最大子团求解中保持群体多样性能力不足、早熟、耗时长、成功率低等缺陷,利用随机抽样方法对交叉操作进行重新设计,结合免疫机理定义染色体浓度,设计克隆选择策略,提出了求解最大子团问题的随机抽样免疫遗传算法。用仿真算例说明了新算法在解的质量、收敛速度等各项指标上均有提高,且不比DLS-MC、QUALEX等经典搜索算法差,对某些算例还得到了更好解。
Aiming at the defects of genetic algorithm for the maximum clique problem in the deficiency of keeping population diversity,prematurity,time consuming,low success rate and so on,the crossover operation in GA is redesigned by random sampling.Combined with immune mechanism,chromosome concentration is defined and clonal selection strategy is designed,thus a Immune Genetic Algorithm is given based on random sampling for solving the maximum clique problem.The emulational examples show that solution quality,convergence rate and other various indices are improved by the new algorithm.On the other hand,the new algorithm is not inferior to such classical search algorithms as DLS-MC and QUALEX,and it gets better solutions to some examples.
出处
《计算机工程与应用》
CSCD
北大核心
2011年第16期40-42,107,共4页
Computer Engineering and Applications
基金
安徽省高校省级自然科学研究重点项目(No.KJ2011A267)
关键词
最大团问题
遗传算法
随机抽样
人工免疫系统
随机抽样免疫遗传算法
Maximum Clique Problem（MCP）
Genetic Algorithm（GA）
Random Sampling（RS）
Artificial Immune Systems（AIS）
Immune Genetic Algorithm based on Random Sampling（RIGA）
