为解决现有k-modes聚类方法因忽略了变量属性之间的弱相关性,常造成其在实际应用中聚类性能不佳的问题,提出一种包含属性弱相关性的新k-modes聚类方法。引入最大信息系数(maximum information coefficient,MIC)度量数据集中变量属性之...为解决现有k-modes聚类方法因忽略了变量属性之间的弱相关性,常造成其在实际应用中聚类性能不佳的问题,提出一种包含属性弱相关性的新k-modes聚类方法。引入最大信息系数(maximum information coefficient,MIC)度量数据集中变量属性之间的相关性;将得到的MIC值与原有距离进行融合,建立包含属性弱相关性信息的新度量方法,以增强变量属性间相关信息的完备性,建立更加精细的k-modes聚类方法;调用3种不同的数据集,将新方法与原有的k-modes聚类方法和其他改进k-modes聚类方法的性能进行对比,并通过仿真结果表明了新方法的有效性。展开更多
This paper presents a novel general method for computing optimal motions of an industrial robot manipulator (AdeptOne XL robot) in the presence of fixed and oscillating obstacles. The optimization model considers th...This paper presents a novel general method for computing optimal motions of an industrial robot manipulator (AdeptOne XL robot) in the presence of fixed and oscillating obstacles. The optimization model considers the nonlinear manipulator dynamics, actuator constraints, joint limits, and obstacle avoidance. The problem has 6 objective functions, 88 variables, and 21 constraints. Two evolutionary algorithms, namely, elitist non-dominated sorting genetic algorithm (NSGA-II) and multi-objective differential evolution (MODE), have been used for the optimization. Two methods (normalized weighting objective functions and average fitness factor) are used to select the best solution tradeoffs. Two multi-objective performance measures, namely solution spread measure and ratio of non-dominated individuals, are used to evaluate the Pareto optimal fronts. Two multi-objective performance measures, namely, optimizer overhead and algorithm effort, are used to find the computational effort of the optimization algorithm. The trajectories are defined by B-spline functions. The results obtained from NSGA-II and MODE are compared and analyzed.展开更多
文摘为解决现有k-modes聚类方法因忽略了变量属性之间的弱相关性,常造成其在实际应用中聚类性能不佳的问题,提出一种包含属性弱相关性的新k-modes聚类方法。引入最大信息系数(maximum information coefficient,MIC)度量数据集中变量属性之间的相关性;将得到的MIC值与原有距离进行融合,建立包含属性弱相关性信息的新度量方法,以增强变量属性间相关信息的完备性,建立更加精细的k-modes聚类方法;调用3种不同的数据集,将新方法与原有的k-modes聚类方法和其他改进k-modes聚类方法的性能进行对比,并通过仿真结果表明了新方法的有效性。
文摘This paper presents a novel general method for computing optimal motions of an industrial robot manipulator (AdeptOne XL robot) in the presence of fixed and oscillating obstacles. The optimization model considers the nonlinear manipulator dynamics, actuator constraints, joint limits, and obstacle avoidance. The problem has 6 objective functions, 88 variables, and 21 constraints. Two evolutionary algorithms, namely, elitist non-dominated sorting genetic algorithm (NSGA-II) and multi-objective differential evolution (MODE), have been used for the optimization. Two methods (normalized weighting objective functions and average fitness factor) are used to select the best solution tradeoffs. Two multi-objective performance measures, namely solution spread measure and ratio of non-dominated individuals, are used to evaluate the Pareto optimal fronts. Two multi-objective performance measures, namely, optimizer overhead and algorithm effort, are used to find the computational effort of the optimization algorithm. The trajectories are defined by B-spline functions. The results obtained from NSGA-II and MODE are compared and analyzed.