In this paper we address the problem of working paths optimization in survivable MPLS network. We focus on an existing facility network, in which only network flows can be optimized to provide network survivability using the local repair strategy. The main goal of our work is to develop an effective evolutionary algorithm (EA) for considered optimization problem. The novelty is that the propose...

Reinforcement Learning (RL) aims to learn an optimal policy for a Markov Decision Process (MDP). For complex, high-dimensional MDPs, it may only be feasible to represent the policy with function approximation. If the policy representation used cannot represent good policies, the problem is misspecified and the learned policy may be far from optimal. We introduce IHOMP as an approach for solving...

While IP is declared as the key technology of the future’s wired and mobile communication, the currently used version of IP, IPv4 itself is not suitable to be used in mobile scenarios. Next generation mobile users require special support to provide connectivity, although they change their place of attachment to the network frequently. The task of mobility management is to provide this support, ...

This paper focuses on architecture-aware source-level code transformation methods for low energy consumption in complex VLIW processors. Though energy issue has long been addressed at hardware level, but a significant contribution of software applications can not be ignored. Successive energy-cycle cost guarded parametric optimization are applied in proposed iterative compilation environment to...

Building robust and reliable storage systems and memory hierarchies is very important in modern computing environment because hierarchical memory architecture is present almost everywhere. This hierarchy is not limited to attached devices, but it expands to middle-tiers, web caches, and network and internet storage. So optimizing access of the data stored along all these hierarchical levels is ...

Clustering is often used for discovering structure in data. Clustering systems diier in the objective function used to evaluate clustering quality and the control strategy used to search the space of clusterings. Ideally, the search strategy should consistently construct clusterings of high quality, but be computationally inexpensive as well. In general, we cannot have it both ways, but we can ...

Clustering is often used to discover structure in data. Clustering systems differ in the objective function used to evaluate clustering quality and the control strategy used to search the space of clusterings. In general, a search strategy cannot both (1) consistently construct clusterings of high quality and (2) be computationally inexpensive. However, we can partition the search so that a sys...

1. Abstract The aim of this work is to perform hierarchical optimization [1] in laminated composite structures, considering simultaneously macroscopic and microscopic levels in the design of the structure and its material. In the macroscopic level, the optimization algorithm cares with orientations and fiber volume fractions of unidirectionally reinforced composite material layers. In the micro...

In this paper, we propose a distributed control strategy for the design of an energy market. The method relies on a hierarchical structure of aggregators for the coordination of prosumers (agents which can produce and consume energy). The hierarchy reflects the voltage level separations of the electrical grid and allows aggregating prosumers in pools, while taking into account the grid operatio...

This paper studies the Hierarchical Modulation, a transmission strategy of the approaching scalable multimedia over frequencyselective fading channel for improving the perceptible quality. An optimization strategy for Hierarchical Modulation and convolutional encoding, which can achieve the target bit error rates with minimum global signal-to-noise ratio in a single-user scenario, is suggested....