Dynamic Multi-Objective Optimization With jMetal and Spark: a Case Study

Technologies for Big Data and Data Science are receiving increasing research interest nowadays. This paper introduces the prototyping architecture of a tool aimed to solve Big Data Optimization problems. Our tool combines the jMetal framework for multi-objective optimization with Apache Spark, a technology that is gaining momentum. In particular, we make use of the streaming facilities of Spark to feed an optimization problem with data from different sources. We demonstrate the use of our tool by solving a dynamic bi-objective instance of the Traveling Salesman Problem (TSP) based on near real-time traffic data from New York City, which is updated several times per minute. Our experiment shows that both jMetal and Spark can be integrated providing a software platform to deal with dynamic multi-optimization problems.

Phases Dynamic Balancer

Most of the current domestic installations are single phase, with contracted power equal to or less than 15 kW and with a potential difference of 230 V. When consumption is expected to be higher you choose to use three different alternating currents with a difference voltage of 400 V between them, which are called phases. This enables the subdivision of the installation in different single-phase circuits, fed independently with the neutral installation. These couples have, in turn, a difference in voltage of 230 V. The neutral is common for all three phases so that, if the system is balanced, no current flows through it. The problem with these installations is that they are designed to work in an offset manner, using phase loads, and simultaneously an equal amount of energy consumed by the three phases of the network. Connection to each of the phases makes independent single-phase loads or disturbance of the operation of the original phase circuit and, consequently, the corresponding increases in consumption, heating of engines, etc.

On the optimaL response of q-vortex

Wingtip vortices represent a hazard for the stability of the following airplane in airport highways. These flows have been usually modeled as swirling jets/wakes, which are known to be highly unstable and susceptible to breakdown at high Reynolds numbers for certain flow conditions, but different to the ones present in real flying airplanes. A very recent study based on Direct Numerical Simulations (DNS) shows that a large variety of helical responses can be excited and amplified when a harmonic inlet forcing is imposed. In this work, the optimal response of q-vortex (both axial vorticity and axial velocity can be modeled by a Gaussian profile) is studied by considering the time-harmonically forced problem with a certain frequency ω. We first reproduce Guo and Sun’s results for the Lamb-Oseen vortex (no axial flow) to validate our numerical code. In the axisymmetric case m = 0, the system response is the largest when the input frequency is null. The axial flow has a weak influence in the response for any axial velocity intensity. We also consider helical perturbations |m| = 1. These perturbations are excited through a resonance mechanism at moderate and large wavelengths as it is shown in Figure 1. In addition, Figure 2 shows that the frequency at which the optimal gain is obtained is not a continuous function of the axial wavenumber k. At smaller wavelengths, large response is excited by steady forcing. Regarding the axial flow, the unstable response is the largest when the axial velocity intensity, 1/q, is near to zero. For perturbations with higher azimuthal wavenumbers |m| > 1, the magnitudes of the response are smaller than those for helical modes. In order to establish an alternative validation, DNS has been carried out by using a pseudospectral Fourier formulation finding a very good agreement.