A fuzzified systematic adjustment of the robotic Darwinian PSO

The Darwinian Particle Swarm Optimization (DPSO) is an evolutionary algorithm that extends the Particle Swarm Optimization using natural selection to enhance the ability to escape from sub-optimal solutions. An extension of the DPSO to multi-robot applications has been recently proposed and denoted as Robotic Darwinian PSO (RDPSO), benefiting from the dynamical partitioning of the whole population of robots, hence decreasing the amount of required information exchange among robots. This paper further extends the previously proposed algorithm adapting the behavior of robots based on a set of context-based evaluation metrics. Those metrics are then used as inputs of a fuzzy system so as to systematically adjust the RDPSO parameters (i.e., outputs of the fuzzy system), thus improving its convergence rate, susceptibility to obstacles and communication constraints. The adapted RDPSO is evaluated in groups of physical robots, being further explored using larger populations of simulated mobile robots within a larger scenario.

The influence of pre-departure training on expatriate adjustment: an empirical investigation with portuguese international assignees

This chapter examines the cross-cultural influence of training on the adjustment of international assignees. We focus on the pre-departure training (PDT) before an international assignment. It is an important topic because in the globalized world of today more and more expatriations are needed. The absence of PDT may generate the failure of the expatriation experience. Companies may neglect PDT due to cost reduction practices and ignorance of the need for it. Data were collected through semi-structured interviews to 42 Portuguese international assignees and 18 organizational representatives from nine Portuguese companies. The results suggest that companies should develop PDT programs, particularly when the cultural distance to the host country is bigger and when there is no previous experience of expatriation to that country in the company. The study is original because it details in depth the methods of PDT, its problems, and consequences. Some limitations linked to the research design and detailed in the conclusion should be overcome in future studies.

Determining the Adjustment Baseline Parameters to Define an Accurate Customer Baseline Load

Demand response is an energy resource that has gained increasing importance in the context of competitive electricity markets and of smart grids. New business models and methods designed to integrate demand response in electricity markets and of smart grids have been published, reporting the need of additional work in this field. In order to adequately remunerate the participation of the consumers in demand response programs, improved consumers’ performance evaluation methods are needed. The methodology proposed in the present paper determines the characterization of the baseline approach that better fits the consumer historic consumption, in order to determine the expected consumption in absent of participation in a demand response event and then determine the actual consumption reduction. The defined baseline can then be used to better determine the remuneration of the consumer. The paper includes a case study with real data to illustrate the application of the proposed methodology.