Search results

Nowadays, there are strong movements towards development and usage of multimedia courseware as a means of knowledge transfer. Many authors of textbooks or lecture notes are now striving to redesign the supporting material for their major courses in a structured, highly efficient way, including interactive content and media. Thus, in order to avoid unnecessary work load resulting from updating and publishing various courseware versions, tools for improving document creation and conversion have been developed and are now being applied for the first time on a new “Electrodynamics”‐‐ courseware.

The aim of this paper is to reduce the evaluations number of the fine model within the output space mapping (OSM) technique in order to reduce their computing time.

In this paper, n-level OSM is proposed and expected to be even faster than the conventional OSM. The proposed algorithm takes advantages of the availability of n models of the device to optimize, each of them representing an optimal trade-off between the model error and its computation time. Models with intermediate characteristics between the coarse and fine models are inserted within the proposed algorithm to reduce the number of evaluations of the consuming time model and then the computing time. The advantages of the algorithm are highlighted on the optimization problem of superconducting magnetic energy storage (SMES).

A major computing time gain equals to three is achieved using the n-level OSM algorithm instead of the conventional OSM technique on the optimization problem of SMES.

The originality of this paper is to investigate several models with different granularities within OSM algorithm in order to reduce its computing time without decreasing the performance of the conventional strategy.

The purpose of this paper is to propose a multi-objective optimization algorithm, which can improve both the performance robustness and the constraint feasibility when the uncertainty in design variables is considered.

Multi-objective robust optimization by gradient index combined with the reliability-based design optimization (RBDO).

It is shown that searching for the optimal design of the TEAM problem 22, which can minimize the magnetic stray field by keeping the target system energy (180 MJ) and improve the feasibility of superconductivity constraint (quenching condition), is possible by using the proposed method.

RBDO method applied to the electromagnetic problem cooperated with the design sensitivity analysis by the finite element method.

The purpose of this paper is to extend a (μ/ρ, λ) evolution strategy to perform remarkably more globally and to detect as many solutions as possible close to the Pareto optimal front.

A C‐link cluster algorithm is used to group the parameter configurations of the current population into more or less independent clusters. Following this procedure, recombination (a classical operator of evolutionary strategies) is modified. Recombination within a cluster is performed with a higher probability than recombination of individuals coming from detached clusters.

It is shown that this new method ends up virtually always in the global solution of a multi‐modal test function. When applied to a real‐world application, several solutions very close to the front of Pareto optimal solutions are detected.

Stochastic optimization strategies need a very large number of function calls to exhibit their ability to reach very good local if not the global solution. Therefore, the application of such methods is still limited to problems where the forward solutions can be obtained with a reasonable computational effort.

The main improvement is the usage of approximate number of isolated clusters to dynamically update the size of the population in order to save computation time, to find the global solution with a higher probability and to use more than one objective function to cover a larger part of the Pareto optimal front.