Abstract: A surrogate-based method for efficient multi-objective antenna optimization is presented. Our technique exploits response surface approximation (RSA) model constructed from sampled low-fidelity antenna model (here, obtained through coarse-discretization EM simulation). The RSA model enables fast determination of the best available trade-offs between conflicting design goals. A low-cost RSA model construction is possible through initial reduction of the design space. Optimization of the RSA model has been carried out using a multi-objective evolutionary algorithm (MOEA). Additional response correction techniques have been subsequently applied to improve selected designs at the high-fidelity EM antenna model level. The refined designs constitute the final Pareto set representation. The proposed approach has been validated using an ultra-wideband (UWB) monocone and a planar Yagi-Uda antenna.

Abstract: The second order Ordinary Differential Equation (ODE) system obtained after semidiscretizing the wave-type partial differential equation (PDE) with the finite element method (FEM) shows strong numerical stiffness. Its resolution requires the use of numerical methods with good stability properties and controlled numerical dissipation in the high-frequency range. The HHT-$\alpha$ and BDF-$\alpha$ methods are second order precision, unconditionally stable and able to dissipate high-modes for some values of the parameters. The finite element method has been applied to the one-dimensional linear wave-type PDE and to a non-linear version of a string of a guitar. The ODE systems obtained after applying FEM are solved by these two methods, proving that both are able to dissipate the high-modes.

Abstract: In this paper, we deal with problems of efficient resource management and scheduling in utility Grids. There are global job flows from external users along with resource owners’ local tasks upon resource non-dedication condition. Competition for resource reservation between independent users, local and global job flows substantially complicates scheduling and the requirement to provide the necessary quality of service. A meta-scheduling model, justified in this work, assumes a complex combination of job flow dispatching and application-level scheduling methods for jobs, as well as resource sharing and consumption policies established in virtual organizations (VOs) and based on economic principles. A solution to the problem of fair resource sharing among VO stakeholders with simulation studies is proposed.

Abstract: In this paper we propose a hybrid metaheuristic based on General Variable Neighbor- hood search and Integer Linear Programming for solving the vehicle routing problem with time windows (VRPTW).The problem consists in determining the minimum cost routes for a homogeneous fleet of vehicles to meet the demand of a set of customers within a specified time windows. The proposed heuristic, called VNS-SCP is considered as a matheuristic where the hybridization of heuristic (VNS) and exact (Set Covering Problem (SCP)) method is used in this approach as an intertwined collaborative cooperation manner. In this approach an initial solution is first created using Solomon route-construction heuristics, the nearest neighbor algorithm. In the second phase the solutions are improved in terms of the total distance traveled using VNS-SCP. The algorithm is tested using Solomon benchmark. Our findings indicate that the proposed procedure outperforms other local searches and metaheuristics.

Abstract: A robust simulation-driven methodology for rapid and reliable design of RF/microwave circuits comprising compact microstrip resonant cells (CMRCs) is presented. We introduce a nested space mapping (NSM) technology, in which the inner space mapping layer is utilized to improve the generalization capabilities of the equivalent circuit model corresponding to a constitutive element of the circuit under consideration. The outer layer enhances the surrogate model of the entire structure under design. We demonstrate that NSM significantly improves performance of surrogate-based optimization of composite RF/microwave structures. It is validated using two examples of UWB microstrip matching transformers (MTs) and compared to other competitive surrogate-assisted methods attempting to solve the problem of compact RF/microwave component design.