Xinwu Yang | Searcharxiv

arXiv2026

Well-posedness and existence of an invariant measure for the linearly-damped KdV equations driven by a jump noise

In this paper, we investigate the linearly damped KdV equation on the one-dimensional torus $\mathbb{T}$, perturbed by a multiplicative L\'{e}vy noise. For any damping coefficient $\gamma > 0$, we establish the existence and uniqueness of a pathwise weak solution with values in $H^2(\mathbb{T})$. In the second part of the paper, we analyze the long-time behavior of these solutions. This study is particularly subtle as the presence of jumps in time can significantly influence the asymptotics. We show, using the techniques of Maslowski and Seidler, that, provided the frictional damping coefficient $\gamma > 0$ is sufficiently large, the system influenced by square-integrable jumps admits an invariant measure in $H^2(\mathbb{T})$.

math.AP↗

arXiv2019

An Improved multi-objective genetic algorithm based on orthogonal design and adaptive clustering pruning strategy

Two important characteristics of multi-objective evolutionary algorithms are distribution and convergency. As a classic multi-objective genetic algorithm, NSGA-II is widely used in multi-objective optimization fields. However, in NSGA-II, the random population initialization and the strategy of population maintenance based on distance cannot maintain the distribution or convergency of the population well. To dispose these two deficiencies, this paper proposes an improved algorithm, OTNSGA-II II, which has a better performance on distribution and convergency. The new algorithm adopts orthogonal experiment, which selects individuals in manner of a new discontinuing non-dominated sorting and crowding distance, to produce the initial population. And a new pruning strategy based on clustering is proposed to self-adaptively prunes individuals with similar features and poor performance in non-dominated sorting and crowding distance, or to individuals are far away from the Pareto Front according to the degree of intra-class aggregation of clustering results. The new pruning strategy makes population to converge to the Pareto Front more easily and maintain the distribution of population. OTNSGA-II and NSGA-II are compared on various types of test functions to verify the improvement of OTNSGA-II in terms of distribution and convergency.

cs.NE↗