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Evolving dynamic multiple-objective optimization problems with objective replacement
This paper studies the strategies for multi-objective optimization in a dynamic environment. In particular, we focus on problems with objective replacement, where some objectives may be replaced with new objectives during evolution. It is shown that the Pareto-optimal sets before and after the objective replacement share some common members. Based on this observation, we suggest the inheritance strategy. When objective replacement occurs, this strategy selects good chromosomes according to the new objective set from the solutions found before objective replacement, and then continues to optimize them via evolution for the new objective set. The experiment results showed that this strategy can help MOGAs achieve better performance than MOGAs without using the inheritance strategy, where the evolution is restarted when objective replacement occurs. More solutions with better quality are found during the same time span
On the flag curvature of Finsler metrics of scalar curvature
The flag curvature of a Finsler metric is called a Riemannian quantity
because it is an extension of sectional curvature in Riemannian geometry. In
Finsler geometry, there are several non-Riemannian quantities such as the
(mean) Cartan torsion, the (mean) Landsberg curvature and the S-curvature,
which all vanish for Riemannian metrics. It is important to understand the
geometric meanings of these quantities. In this paper, we study Finsler metrics
of scalar curvature (i.e., the flag curvature is a scalar function on the slit
tangent bundle) and partially determine the flag curvature when certain
non-Riemannian quantities are isotropic. Using the obtained formula for the
flag curvature, we classify locally projectively flat Randers metrics with
isotropic S-curvature.Comment: 23 page
Fast Reachable Set Approximations via State Decoupling Disturbances
With the recent surge of interest in using robotics and automation for civil
purposes, providing safety and performance guarantees has become extremely
important. In the past, differential games have been successfully used for the
analysis of safety-critical systems. In particular, the Hamilton-Jacobi (HJ)
formulation of differential games provides a flexible way to compute the
reachable set, which can characterize the set of states which lead to either
desirable or undesirable configurations, depending on the application. While HJ
reachability is applicable to many small practical systems, the curse of
dimensionality prevents the direct application of HJ reachability to many
larger systems. To address computation complexity issues, various efficient
computation methods in the literature have been developed for approximating or
exactly computing the solution to HJ partial differential equations, but only
when the system dynamics are of specific forms. In this paper, we propose a
flexible method to trade off optimality with computation complexity in HJ
reachability analysis. To achieve this, we propose to simplify system dynamics
by treating state variables as disturbances. We prove that the resulting
approximation is conservative in the desired direction, and demonstrate our
method using a four-dimensional plane model.Comment: in Proceedings of the IEE Conference on Decision and Control, 201
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