多阶段复合型遗传算法的结构及性能研究  

Structure of multi-stage composite genetic algorithm and its performance

作　　者：刘立民[1] 潘伟[1] 庞彦军[1] 李法朝[2] 

机构地区：[1]河北工程大学理学院,河北邯郸056038 [2]河北科技大学经济管理学院,河北石家庄050018

出　　处：《河北工程大学学报（自然科学版）》2010年第2期107-112,共6页Journal of Hebei University of Engineering:Natural Science Edition

基　　金：河北省自然科学基金项目(编目编号:F2009000857)

摘　　要：针对基本遗传算法(SGA)在求解复杂优化问题时常常存在的局部收敛和不能很好地收敛到全局最优解的不足,采用逐步缩小寻优范围的策略,提出了多阶段复合型遗传算法(简记为MSC-GA),给出了MSC-GA的结构以及具体的实施策略;进而利用Markov链理论和仿真技术分析了MSC-GA的收敛性,证明了MSC-GA在最优个体保留策略下是全局收敛的;最后,通过一个实例从不同的角度分析了MSC-GA的收敛性能。结果表明,MSC-GA具有良好的收敛稳定性,可以有效地避免局部收敛现象,尤其适用于大范围、高精度的优化问题,具有广泛的应用价值。In view of the slowness and the locality of convergence for Simple Genetic Algorithm （SGA for short） in solving complex optimization problems, we propose an improved genetic algorithm named multi - stage composite genetic algorithm （MSC - GA for short） by reducing the optimization - search range gradually, and give the structure and the implementation steps of MSC - GA; then we consider its global convergence under the elitist preserving strategy using Markov chain theory, and analyze its performance through three examples from different aspects. All these indicate that the new algorithm possess interesting advan- tages such as better convergence, less chance trapping into premature states, which can be widely used in many large - scale and high- accuracy optimization problems.

关 键 词：遗传算法 多阶段复合型遗传算法(MSC-GA) 收敛性 MARKOV链 优化 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]