564 research outputs found
An Improved Multiobjective Particle Swarm Optimization Algorithm Using Minimum Distance of Point to Line
In a multiobjective particle swarm optimization algorithm, selection of the global best particle for each particle of the population from a set of Pareto optimal solutions has a significant impact on the convergence and diversity of solutions, especially when optimizing problems with a large number of objectives. In this paper, a new method is introduced for selecting the global best particle, which is minimum distance of point to line multiobjective particle swarm optimization (MDPL-MOPSO). Using the basic concept of minimum distance of point to line and objective, the global best particle among archive members can be selected. Different test functions were used to test and compare MDPL-MOPSO with CD-MOPSO. The result shows that the convergence and diversity of MDPL-MOPSO are relatively better than CD-MOPSO. Finally, the proposed multiobjective particle swarm optimization algorithm is used for the Pareto optimal design of a five-degree-of-freedom vehicle vibration model, which resulted in numerous effective trade-offs among conflicting objectives, including seat acceleration, front tire velocity, rear tire velocity, relative displacement between sprung mass and front tire, and relative displacement between sprung mass and rear tire. The superiority of this work is demonstrated by comparing the obtained results with the literature
An Adaptive Deep RL Method for Non-Stationary Environments with Piecewise Stable Context
One of the key challenges in deploying RL to real-world applications is to
adapt to variations of unknown environment contexts, such as changing terrains
in robotic tasks and fluctuated bandwidth in congestion control. Existing works
on adaptation to unknown environment contexts either assume the contexts are
the same for the whole episode or assume the context variables are Markovian.
However, in many real-world applications, the environment context usually stays
stable for a stochastic period and then changes in an abrupt and unpredictable
manner within an episode, resulting in a segment structure, which existing
works fail to address. To leverage the segment structure of piecewise stable
context in real-world applications, in this paper, we propose a
\textit{\textbf{Se}gmented \textbf{C}ontext \textbf{B}elief \textbf{A}ugmented
\textbf{D}eep~(SeCBAD)} RL method. Our method can jointly infer the belief
distribution over latent context with the posterior over segment length and
perform more accurate belief context inference with observed data within the
current context segment. The inferred belief context can be leveraged to
augment the state, leading to a policy that can adapt to abrupt variations in
context. We demonstrate empirically that SeCBAD can infer context segment
length accurately and outperform existing methods on a toy grid world
environment and Mujuco tasks with piecewise-stable context.Comment: NeurIPS 202
Martensitic transformation, electronic structure and magnetism in D0(3)-ordered Heusler Mn(3)Z (Z = B, Al, Ga, Ge, Sb) alloys
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