Taguchi's method is a quality design technique whose applications in numerical single-objective optimization have been recently exploited. In this article, a novel multiobjective (MO) algorithm based on Taguchi's technique is illustrated and its performances assessed. Validation is performed through a comparison between the presented algorithm and a MO genetic algorithm (GA) based optimization, first on different sets of test functions and then on a practical antenna array synthesis problem. Results indicate a generally better behavior of the proposed algorithm in terms of convergence and spreading over the Pareto front with respect to the GA benchmark.
A recently developed material based on carbon nanotubes is used here for the realization of single-and double-layered frequency-selective surfaces (FSSs) with relevant absorbing properties. The peculiar characteristics of carbon nanotubes are exploited to devise high-loss resonant ring structures periodically arranged to build the FSS. By introducing two layers of rings, an absorber with stable characteristics over a wide frequency band and over a wide range for the incident wave angle is achieved.
Polyomino-based arrays allow to efficiently exploit the available array area with a regular element lattice, yet exhibit a nonuniform distribution of their phase centers, leading to superior electronic scanning capabilities. Yet polyomino arrays are usually implemented via polyomino of equal order, leading to uniform amplitude distribution and poor side lobe levels. In this contribution, a tiling of polyominoes of different orders is proposed to attain at the same time good scanning characteristics and side lobe level.
Designing an electronic system is an engineering task bound to the satisfaction of a given set of requirements. If it is possible to express the compliance of the system to the requirements in a mathematical form giving a measure of how much a given system is far from compliance, then it would be possible to obtain the desired performances by seeking for the minimum of such a function. Seeking the minimum of a function is a daunting task to which a whole branch of mathematics, optimization, is devoted. In the case in which the design must comply to several requirements, it is usually more practical and interesting to define a set of functions to be minimized altogether. In this case the search for an optimal point, or a set of points, can be performed by applying a multi‐objective optimization technique. This chapter will investigate popular multi‐objective optimization techniques, giving both their basics and describing recent advancements. For the selected techniques, performances over a set of benchmarks will be presented, as well as results over relevant electronic engineering problems.
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