Abstract

Abstract
In this work we address the problem of generating electricity through Kite Power Systems. We solve an optimal control problem which devises the trajectories and controls for the kite that maximize the total energy produced in a given interval. This is a highly nonlinear problem for which the optimization is challenging. We use a continuous time model of the kite and implement time mesh refinement strategies to solve the problem. We report results that show that with an adaptive mesh refinement strategy the problem can be solved with a high level of accuracy and (in simplified versions) much faster. (C) 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the scientific committee of the 4th International Conference on Energy and Environment Research.

Abstract
Multi-objective optimization evolutionary techniques provide solutions for a specific problem using optimally concepts taking into consideration all the design criteria. In the last years, several multi-objective algorithms were proposed but usually the performance is measured at the end neglecting, therefore, the solution diversity along the interactions. In order to understand the evolution of the solutions this work studies the dynamic of the successive iterations. The analysis adopts the fractional entropy for measuring the statistical behavior of the population. The results show that the entropy is a good tool to monitor and capture phenomena such as the diversity and convergence during the algorithm execution. © Springer International Publishing AG 2017.

Abstract
In the last issue of 2015, the editorial was titled “Winter is coming: The dawn of Innovation?“ As almost 2 years have gone by, this topic was revisited. So why not wonder how innovation was going around this same time of the year? In trying to answer this question, a visit to Google© Trends revealed an interesting and possibly unexpected chart that is illustrated in figure 1. (...)

Abstract

Abstract
We present a novel approach that uses an iterative deepening algorithm in order to perform probabilistic logic inference for ProbLog, a probabilistic extension of Prolog. The most used inference method for ProbLog is exact inference combined with tabling. Tabled exact inference first collects a set of SLG derivations which contain the probabilistic structure of the ProbLog program including the cycles. At a second step, inference requires handling these cycles in order to create a noncyclic Boolean representation of the probabilistic information. Finally, the Boolean representation is compiled to a data structure where inference can be performed in linear time. Previous work has illustrated that there are two limiting factors for ProbLog’s exact inference. The first factor is the target compilation language and the second factor is the handling of the cycles. In this paper, we address the second factor by presenting an iterative deepening algorithm which handles cycles and produces solutions to problems that previously ProbLog was not able to solve. Our experimental results show that our iterative deepening approach gets approximate bounded values in almost all cases and in most cases we are able to get the exact result for the same or one lower scaling factor. © Springer International Publishing AG 2017.