定向多尺度变异克隆选择优化算法  

Clone selection optimization algorithm with directional multi-scale mutation

作　　者：陶新民[1] 刘福荣[2] 刘玉[1] 付丹丹[1] 

机构地区：[1]哈尔滨工程大学信息与通信工程学院,哈尔滨150001 [2]哈尔滨电力职业技术学院信息工程系,哈尔滨150030

出　　处：《控制与决策》2011年第2期175-181,共7页Control and Decision

基　　金：国家自然科学基金项目(61074076);中国博士后科学基金项目(20090450119);中国博士点新教师基金项目(20092304120017);黑龙江省博士后基金项目(LBH-Z08227)

摘　　要：提出一种定向多尺度变异克隆选择优化算法.为了实现抗体间信息共享,算法利用定向进化机制引导抗体向着抗体群最优解区域逼近.采用多尺度高斯变异机制,在算法初期利用大尺度振荡变异实现了全局最优解空间的快速定位.随着适应值的提升,小尺度变异会随之减低,使得算法在进化后期通过小尺度变异完成局部精确解的搜索.将算法应用到5个经典函数优化问题,结果表明,该算法不仅具有更快的收敛速度,而且全局解搜索能力和稳定性均有显著提高.To deal with the problem of single-scale mutation,premature convergence and slow search speed,a clone selection algorithm（CSA） with directional multi-scale gaussian mutation is proposed.To implement the share of information between antibodies,the directional evolution mechanism is utilized to induce the antibodies to evolve to the best solution region.The special multi-scale Gaussian mutation operators are introduced to make antibodies explore the search space more efficiently.The large-scale mutation operators can be utilized to quickly localize the global optimized space at the early evolution,while the small-scale mutation operators can implement local accurate minima solution search at the late evolution,which can make the algorithm explore the global and local minima thoroughly at the same time.The comparison of the performance of the proposed approach with other CSAs with different mutations is experimented.The experimental results show that the proposed method can not only effectively solve the premature convergence problem,but also significantly speed up the convergence and improve the stability.

关 键 词：克隆选择 多尺度变异 定向进化 勘探和开采 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]