Abstract
Nowadays, wind energy has an important role in the challenges of clean energy supply. It is the fastest growing energy source with a increasing annual rate of 20%. This scenario motivate the development of an optimization design tool to find optimal layout for wind farms. This paper proposes a mathematical model to find the best electrical interconnection configuration of the wind farm turbines and the substation. The goal is to minimize the installation costs, that include cable cost and cable installation costs, considering technical constraints. This problem corresponds to a capacitated minimum spanning tree with additional constraints. The methodology proposed is applied in a real case study and the results are compared with the ground solution. © Springer International Publishing Switzerland 2014.

Abstract
The particle swarm optimization (PSO), one of the most successful natural inspired algorithms, is revisited in the context of a proposal for a new teaching experiment. The problem considered is the open-loop step identification procedure, which is studied as an optimization problem. The PSO canonical algorithm main issues addressed within the proposed open-loop step identification experience are: the swarm random initialization methodology, the population size variation, and the inertia weight selection. The teaching experience learning outcomes are stated, simulation results presented, and feedback results from students analyzed. (c) 2011 Wiley Periodicals, Inc. Comput Appl Eng Educ 22:227-237, 2014; View this article online at ; DOI

Abstract
The diffusion of innovation theory aims to explain how new ideas and practices are disseminated among social system members. A significant number of the existing models is based on the use of parameters which determine the process of innovation adoption, and rely on simple mathematical functions centered in the observation and description of diffusion patterns. These models enable a more explicit diffusion process study, but their use involves the estimation of diffusion coefficients, usually obtained from historical data or chronological series. This raises some application problems in contexts where there is no data or the data is insufficient. This paper proposes the use of evolutionary computation as an alternative approach for the simulation of innovation diffusion within organizations. To overcome some of the problems inherent to existing models an evolutionary algorithm is proposed based on a probabilistic approach. The results of the simulations that were done to validate the algorithm revealed to be very promissing in this context. Simulation experiment results are presented that reveals a very promising approach of the proposed model. © 2013 Springer-Verlag Berlin Heidelberg.

Abstract
Multi-objective optimization inspired on genetic algorithms are population based search methods. The population elements, chromosomes, evolve using inheritance, mutation, selection and crossover mechanisms. The aim of these algorithms is to obtain a representative non-dominated Pareto front from a given problem. Several approaches to study the convergence and performance of algorithm variants have been proposed, particularly by accessing the final population. In this work, a novel approach by analyzing multi-objective algorithm dynamics during the algorithm execution is considered. The results indicate that Shannon entropy can be used as an algorithm indicator of diversity and convergence.

Abstract
Multidimensional systems, or n-D systems, are systems having several independent variables. Several topics, in particular stability, of n-D systems (n > 1) have attracted the interest of many researchers. The main reason, is because the extension stability theory of 1-D systems to systems with higher dimensions is not straightforward. In this paper, two adopted meta-heuristics algorithms are used for complementing the study of systems stability based on their polynomial characteristics over the variables boundaries. The two meta-heuristics are genetic algorithm and particle swarm optimization due to its popularity. Practical results of both meta-heuristics are compared and the better algorithm highlighted. The results demonstrate that meta-heuristics can be applied in studding multidimensional system stability.

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.