交叉型状态空间模型进化算法的全局收敛性分析  

Analysis of global convergence of crossover evolutionary algorithm based on state-space model

作　　者：王鼎湘 李茂军[1] 李雪[1] 成立[1] 

机构地区：[1]长沙理工大学电气与信息工程学院,长沙410004

出　　处：《计算机应用》2014年第12期3424-3427,共4页journal of Computer Applications

基　　金：国家自然科学基金资助项目(61074018)

摘　　要：基于状态空间模型的进化算法(SEA)是一种新颖的实数编码进化算法,在实际工程优化问题中取得了良好的优化效果。为促进SEA的理论及应用研究,对交叉型SEA(SCEA)的全局收敛性进行了研究,得出SCEA不是全局收敛的结论。通过改变状态进化矩阵的构造方式和提出弹力搜索操作,得到改进交叉型SEA(SMCEA),利用齐次有限Markov链对SMCEA的全局收敛性进行了证明。最后利用两个测试函数对算法进行实验分析,结果表明,SMCEA在收敛速度、最优解搜索能力和运算时间等方面都有较大改善,验证了SMCEA的有效性,得到了SMCEA优于遗传算法(GA)和SCEA的结论。Evolutionary Algorithm based on State-space model （SEA） is a novel real-coded evolutionary algorithm, it has good optimization effects in engineering optimization problems. Global convergence of crossover SEA （SCEA） was studied to promote the theory and application research of SEA. The conclusion that SCEA is not global convergent was drawn. Modified Crossover Evolutionary Algorithm based on State-space Model （SMCEA） was presented by changing the comstruction way of state evolution matrix and introducing elastic search operation. SMCEA is global convergent was proved by homogeneous finite Markov chain. By using two test functions to experimental analysis, the results show that the SMCEA are improved substantially in such aspects as convergence rate, ability of reaching the optimal value and operation time. Then, the effectiveness of SMCEA is proved and that SMCEA is better than Genetic Algorithm （GA） and SCEA was concluded.

关 键 词：状态空间模型 进化算法 交叉算子 弹力搜索 收敛性 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]