共查询到19条相似文献,搜索用时 140 毫秒
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基于双种群的小生境差分进化算法 总被引:2,自引:0,他引:2
将非线性方程组的求解问题转化为函数优化问题,当方程组有多个解时,它的适应值函数就是具有多个最优解的多峰函数.为此,提出了基于双种群的小生境差分进化算法.在该算法中,进化在两个不同的子群间并行进行,通过使用不同的变异策略,实现种群在解空间具有尽可能分散的探索能力的同时在局部具有尽可能细致的搜索能力.通过子群重组实现子群间的信息交换,然后引入小生境淘汰机制.对典型测试函数的优化结果表明,该算法能找到全部解,而且精度好. 相似文献
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针对PSO聚类算法需要预定聚类中心个数的问题,提出一种变维搜索解空间的量子粒子群优化聚类算法.该算法采用量子编码的方式实现双链并行搜索,加速寻优过程,避免了粒子在解空间边界过分聚集;设计了幅角相位旋转算子和变异算子,使幅角相位依变概率进行变异,提高了粒子群的多样性;在迭代过程中,动态更新了聚类中心的数量,使算法能够在不同维度的解空间中寻优.仿真实验表明,该算法的收敛速度和聚类精度得到一定的改善. 相似文献
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基于混合并行遗传算法的文本聚类研究 总被引:2,自引:0,他引:2
针对传统K-Means聚类算法对初始聚类中心的选择敏感,易陷入局部最优解的问题,提出一种基于混合并行遗传算法的文本聚类方法。该方法首先将文档集合表示成向量空间模型,并在文档向量中随机选择初始聚类中心形成染色体,然后结合K-Means算法的高效性和并行遗传算法的全局优化能力,通过种群内的遗传、变异和种群间的并行进化、联姻,有效地避免了局部最优解的出现。实验表明该算法相对于K-Means算法、简单遗传算法等文本聚类方法具有更高的精确度和全局寻优能力。 相似文献
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定向多尺度变异克隆选择优化算法 总被引:2,自引:1,他引:1
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. 相似文献
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演化算法中有很多不同的演化算子,每一种算子对于不同的优化问题都有自己的优点和缺点。提出了一种基于交流模型的多算子混合演化算法。在该算法中,有两个种群,使用两种算子:多父体杂交算子和Cauchy变异算子。种群间的信息交换通过个体交流实现。对23个标准测试函数的数值仿真表明,该算法具有良好的全局收敛性和鲁棒性。 相似文献
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Dabin Zhang Lean Yu Shouyang Wang Yingwen Song 《Frontiers of Computer Science in China》2009,3(2):217-225
A new clustering analysis method based on the pseudo parallel genetic algorithm (PPGA) is proposed for business cycle indicator
selection. In the proposed method, the category of each indicator is coded by real numbers, and some illegal chromosomes are
repaired by the identification and restoration of empty class. Two mutation operators, namely the discrete random mutation
operator and the optimal direction mutation operator, are designed to balance the local convergence speed and the global convergence
performance, which are then combined with migration strategy and insertion strategy. For the purpose of verification and illustration,
the proposed method is compared with the K-means clustering algorithm and the standard genetic algorithms via a numerical
simulation experiment. The experimental result shows the feasibility and effectiveness of the new PPGA-based clustering analysis
algorithm. Meanwhile, the proposed clustering analysis algorithm is also applied to select the business cycle indicators to
examine the status of the macro economy. Empirical results demonstrate that the proposed method can effectively and correctly
select some leading indicators, coincident indicators, and lagging indicators to reflect the business cycle, which is extremely
operational for some macro economy administrative managers and business decision-makers. 相似文献
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布局问题在理沦上属于NPC问题,在工程实践上具有广泛的应用。为较好地求解该问题,该文以并行遗传算法(PGA)为基础,针对其早熟和收敛速度慢两大缺陷加以改进,给出了一种并行混合遗传算法(PHGA).PHGA采用该文提出的压力插他排序选择算子,起到了双重作用:一是在进化初期可以防止早熟;二是在进化后期有利于加快算法的收敛。算法利用混沌初始化可提高初始群体的质量,并依自适应交叉和变异概率值对子群体进行分类,与Powell法混合可以很好地改善算法的局部搜索性能。文中通过标准函数优化和布局设计的算例验证了该算法的可行性和有效性。 相似文献
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针对传统的模糊聚类算法大都针对单一目标函数的优化,而无法获得更全面、更准确的聚类结果的问题,提出一种基于改进多目标萤火虫优化算法的模糊聚类方法。首先在多目标萤火虫算法中引入一种动态调整的变异机制以获得更加均匀分布的非劣解,其中以动态减小的概率选择个体并采用类似于差分进化算法中变异算子的策略对其进行变异,通过自适应调整收缩因子以提高变异效率。然后当归档集中的最优解集充满时,从中选取一定量的解与当前种群组合进行下一次进化,使得算法具有更高的效率。最后将其运用到模糊聚类问题中,通过同时优化两个模糊聚类指标的目标函数并从最终的归档集中选取一个解确定聚类结果。采用5组数据进行实验的结果表明,相对于单目标聚类方法,所提方法对各种数据集的聚类有效性指标提高了2到8个百分点,具有更高的聚类准确性和更好的综合性能。 相似文献
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Differential evolution (DE) is widely studied in the past decade. In its mutation operator, the random variations are derived from the difference of two randomly selected different individuals. Difference vector plays an important role in evolution. It is observed that the best fitness found so far by DE cannot be improved in every generation. In this article, a directional mutation operator is proposed. It attempts to recognize good variation directions and increase the number of generations having fitness improvement. The idea is to construct a pool of difference vectors calculated when fitness is improved at a generation. The difference vector pool will guide the mutation search in the next generation once only. The directional mutation operator can be applied into any DE mutation strategy. The purpose is to speed up the convergence of DE and improve its performance. The proposed method is evaluated experimentally on CEC 2005 test set with dimension 30 and on CEC 2008 test set with dimensions 100 and 1000. It is demonstrated that the proposed method can result in a larger number of generations having fitness improvement than classic DE. It is combined with eleven DE algorithms as examples of how to combine with other algorithms. After its incorporation, the performance of most of these DE algorithms is significantly improved. Moreover, simulation results show that the directional mutation operator is helpful for balancing the exploration and exploitation capacity of the tested DE algorithms. Furthermore, the directional mutation operator modifications can save computational time compared to the original algorithms. The proposed approach is compared with the proximity based mutation operator as both are claimed to be applicable to any DE mutation strategy. The directional mutation operator is shown to be better than the proximity based mutation operator on the five variants in the DE family. Finally, the applications of two real world engineering optimization problems verify the usefulness of the proposed method. 相似文献
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进化算法可以有效地克服K means对初始聚类中心敏感的缺陷,提高了聚类性能。在进化K means聚类算法 (F-EAC)的基础上,针对其变异操作——簇分裂算子的随机性与局部性,提出了两个全局性分裂算子。结合最大最小距离的思想,利用待分裂簇的周边簇信息来指导簇分裂初始点的选择,使簇的分裂更有利于全局划分,以进一步提高进化聚类的有效性。实验结果表明,基于全局性分裂算子的算法在类数发现及聚类精度方面均优于F EAC。 相似文献