Abstract
The continuous changes in electricity markets' mechanisms and operations turn this environment into a challenging domain for the participating entities. Simulation tools are increasingly being used for decision support purposes of such entities. In particular, multi-agent based simulation, which facilitates the modeling of different types of mechanisms and players, is being fruitfully applied to the study of worldwide electricity markets. An effective decision support to market players' negotiations is, however, still not properly reached due to the uncertainty that results from the increasing penetration of renewable generation and the complexity of market mechanisms themselves. In this scope, this paper proposes a novel metalearner that provides decision support to market players in their negotiations. The proposed metalearner uses as input the output of several other market negotiation strategies, which are used to create a new, enhanced response. The final result is achieved through the combination and evolution of the strategies' learning results by applying a genetic algorithm.

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
Hill climbing and simulated annealing are two fundamental search techniques integrating most artificial intelligence and machine learning courses curricula. These techniques serve as introduction to stochastic and probabilistic based metaheuristics. Simulated annealing can be considered a hill-climbing variant with a probabilistic decision. While simulated annealing is conceptually a simple algorithm, in practice it can be difficult to parameterize. In order to promote a good simulated annealing algorithm perception by students, a simulation experiment is reported here. Key implementation issues are addressed, both for minimization and maximization problems. Simulation results are presented.

Abstract

Abstract
The gravitational search algorithm is proposed to design PID control structures. The controller design is performed considering the objectives of set-point tracking and disturbance rejection, minimizing the integral of the absolute error criterion. A two-degrees-of-freedom control configuration with a feed-forward prefilter inserted outside the PID feedback loop is used to improve system performance for both design criteria. The prefilter used is a Posicast three-step shaper designed simultaneously with a PID controller. Simulation results are presented which show the merit of the proposed technique.

Abstract
Wind energy production have been increasing in last years, with an annual growth of the installed capacity rate about 20%. It becomes important to develop optimization techniques to improve the effectiveness of the wind farms. One field in which this can be done is in the distribution network design that interconnects the turbines and the substation. This paper proposes two mathematical models to obtain the optimal electrical interconnection configuration of the wind farm turbines, considering technical constraints. One model minimizes the installation costs and the other one minimizes the installation costs and the energy losses costs registered during the wind farm lifetime. This problem corresponds to a capacitated minimum spanning tree with additional constraints. The proposed models were applied in two real wind farms. A sensitivity analysis is performed over two electrical parameters, the power factor and the load factor. The results show that the electrical losses of the wind farm must be taken into account in the optimization process.