In this paper, we explore a novel ensemble method for spectral clustering. In contrast to the traditional clustering ensemble methods that combine all the obtained clustering results, we propose the adaptive spectral ...In this paper, we explore a novel ensemble method for spectral clustering. In contrast to the traditional clustering ensemble methods that combine all the obtained clustering results, we propose the adaptive spectral clustering ensemble method to achieve a better clustering solution. This method can adaptively assess the number of the component members, which is not owned by many other algorithms. The component clusterings of the ensemble system are generated by spectral clustering （SC） which bears some good characteristics to engender the diverse committees. The selection process works by evaluating the generated component spectral clustering through resampling technique and population-based incremental learning algorithm （PBIL）. Experimental results on UCI datasets demonstrate that the proposed algorithm can achieve better results compared with traditional clustering ensemble methods, especially when the number of component clusterings is large.展开更多
设计一种新的混合蚁群算法。该算法以一种新的二进制蚁群算法为基础,混合PBIL(population based incremental learning)算法及遗传算法的交叉操作和变异操作,从而大大提高了种群的多样性及收敛速度,改善全局最优解的搜索能力。通过函数...设计一种新的混合蚁群算法。该算法以一种新的二进制蚁群算法为基础,混合PBIL(population based incremental learning)算法及遗传算法的交叉操作和变异操作,从而大大提高了种群的多样性及收敛速度,改善全局最优解的搜索能力。通过函数优化测试,表明该算法具有良好的收敛速度和稳定性,最后用于有机物毒性的QSAR研究中,取得较好效果。展开更多
为了提高无线传感器网络节点的三维定位精度,提出了基于人口增量学习(population based incremental learning,PBIL)算法的无线传感器网络三维定位方法;算法首先通过RSSI技术进行测距,设立阈值消除包含误差较大的测量距离,然后使用人口...为了提高无线传感器网络节点的三维定位精度,提出了基于人口增量学习(population based incremental learning,PBIL)算法的无线传感器网络三维定位方法;算法首先通过RSSI技术进行测距,设立阈值消除包含误差较大的测量距离,然后使用人口增量学习算法对适应度函数进行求解,根据Heb规则更新概率向量并产生新的个体,最后经过循环寻优得到最优解;利用MATLAB进行仿真,结果表明:算法的定位精度和稳定性相较于最大似然法有了明显的提高。展开更多
In this paper, two Evolutionary Algorithms (EAs) i.e., an improved Genetic Algorithms (GAs) and Population Based Incremental Learning (PBIL) algorithm are applied for optimal coordination of directional overcurrent re...In this paper, two Evolutionary Algorithms (EAs) i.e., an improved Genetic Algorithms (GAs) and Population Based Incremental Learning (PBIL) algorithm are applied for optimal coordination of directional overcurrent relays in an interconnected power system network. The problem of coordinating directional overcurrent relays is formulated as an optimization problem that is solved via the improved GAs and PBIL. The simulation results obtained using the improved GAs are compared with those obtained using PBIL. The results show that the improved GA proposed in this paper performs better than PBIL.展开更多
In this paper the population based incremental learning method is extended to a form of multiple traits for one gene to reflect pleiotropic and polygenic characters in natural evolved systems and the entropy of a pro...In this paper the population based incremental learning method is extended to a form of multiple traits for one gene to reflect pleiotropic and polygenic characters in natural evolved systems and the entropy of a probability distribution is used to decide the evolvability of the system. This method is used to solve a typical combinatorial optimization problem ─ the symmetric traveling salesman problem. Some results are better than the best existing algorithm of evolutionary algorithms for the problem.展开更多
基金Supported by the National Natural Science Foundation of China (60661003)the Research Project Department of Education of Jiangxi Province (GJJ10566)
文摘In this paper, we explore a novel ensemble method for spectral clustering. In contrast to the traditional clustering ensemble methods that combine all the obtained clustering results, we propose the adaptive spectral clustering ensemble method to achieve a better clustering solution. This method can adaptively assess the number of the component members, which is not owned by many other algorithms. The component clusterings of the ensemble system are generated by spectral clustering （SC） which bears some good characteristics to engender the diverse committees. The selection process works by evaluating the generated component spectral clustering through resampling technique and population-based incremental learning algorithm （PBIL）. Experimental results on UCI datasets demonstrate that the proposed algorithm can achieve better results compared with traditional clustering ensemble methods, especially when the number of component clusterings is large.
文摘设计一种新的混合蚁群算法。该算法以一种新的二进制蚁群算法为基础,混合PBIL(population based incremental learning)算法及遗传算法的交叉操作和变异操作,从而大大提高了种群的多样性及收敛速度,改善全局最优解的搜索能力。通过函数优化测试,表明该算法具有良好的收敛速度和稳定性,最后用于有机物毒性的QSAR研究中,取得较好效果。
文摘为了提高无线传感器网络节点的三维定位精度,提出了基于人口增量学习(population based incremental learning,PBIL)算法的无线传感器网络三维定位方法;算法首先通过RSSI技术进行测距,设立阈值消除包含误差较大的测量距离,然后使用人口增量学习算法对适应度函数进行求解,根据Heb规则更新概率向量并产生新的个体,最后经过循环寻优得到最优解;利用MATLAB进行仿真,结果表明:算法的定位精度和稳定性相较于最大似然法有了明显的提高。
文摘In this paper, two Evolutionary Algorithms (EAs) i.e., an improved Genetic Algorithms (GAs) and Population Based Incremental Learning (PBIL) algorithm are applied for optimal coordination of directional overcurrent relays in an interconnected power system network. The problem of coordinating directional overcurrent relays is formulated as an optimization problem that is solved via the improved GAs and PBIL. The simulation results obtained using the improved GAs are compared with those obtained using PBIL. The results show that the improved GA proposed in this paper performs better than PBIL.
文摘In this paper the population based incremental learning method is extended to a form of multiple traits for one gene to reflect pleiotropic and polygenic characters in natural evolved systems and the entropy of a probability distribution is used to decide the evolvability of the system. This method is used to solve a typical combinatorial optimization problem ─ the symmetric traveling salesman problem. Some results are better than the best existing algorithm of evolutionary algorithms for the problem.
