A new competitive implementation of the electromagnetism-like algorithm for global optimization

The Electromagnetism-like (EM) algorithm is a population- based stochastic global optimization algorithm that uses an attraction- repulsion mechanism to move sample points towards the optimal. In this paper, an implementation of the EM algorithm in the Matlab en- vironment as a useful function for practitioners and for those who want to experiment a new global optimization solver is proposed. A set of benchmark problems are solved in order to evaluate the performance of the implemented method when compared with other stochastic methods available in the Matlab environment. The results con rm that our imple- mentation is a competitive alternative both in term of numerical results and performance. Finally, a case study based on a parameter estimation problem of a biology system shows that the EM implementation could be applied with promising results in the control optimization area.

Social entrepreneurship: does institutional environment make a difference?

The purpose of this paper is to contribute to a better understanding of the link between social entrepreneurs and institutional environment in Portugal. A quantitative approach is used in the study, and primary data were collected through an online survey. A questionnaire was emailed to, both, Portuguese Non-Governmental organizations and projects available on the Portuguese social stock exchange. In the analysis of the data were used descriptive statistics, factorial analysis and t-student tests to validate (or not) the research hypotheses. The results show that a favorable institutional environment has a low importance in the decision to develop social initiatives. This conclusion supports the idea that many social entrepreneurs can emerge even in developing regions where the institutional environment is weak. Therefore, social entrepreneurship could be an instrument of regional development and contribute to attenuate the social and economic differences among Portuguese regions.