Over the past decade, the Air Force Research Laboratory (AFRL) Antenna Technology Branch at Hanscom AFB has employed the simple geneticalgorithm (SGA) as an optimization tool for a wide variety of antenna application...
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Over the past decade, the Air Force Research Laboratory (AFRL) Antenna Technology Branch at Hanscom AFB has employed the simple geneticalgorithm (SGA) as an optimization tool for a wide variety of antenna applications. Over roughly the same period, researchers at the Illinois geneticalgorithm Laboratory (IlliGAL) at the University of Illinois at Urbana Champaign have developed GA design theory and advanced GA techniques called competent genetic algorithms-GAs that solve hard problems quickly, reliably, and accurately. Recently, under the guidance and direction of the Air Force Office of Scientific Research (AFOSR), the two laboratories have formed a collaboration, the common goal of which is to apply simple, competent, and hybrid GA techniques to challenging antenna problems. This paper is composed of two parts. The first part of this paper summarizes previous research conducted by AFRL at Hanscom for which SGAs were implemented to obtain acceptable solutions to several antenna problems. This research covers diverse areas of interest, including array pattern synthesis, antenna test-bed design, gain enhancement, electrically small single bent wire elements, and wideband antenna elements. The second part of this paper starts by briefly reviewing the design theory and design principles necessary for the invention and implementation of fast, scalable geneticalgorithms. A particular procedure, the hierarchical Bayesian optimization algorithm (hBOA) is then briefly outlined, and the remainder of the paper describes collaborative efforts of AFRL and IlliGAL to solve more difficult antenna problems. In particular, recent results of using hBOA to optimize a novel, wideband overlapped subarray system to achieve -35 dB sidelobes over a 20% bandwidth. The problem was sufficiently difficult that acceptable solutions were not obtained using SGAs. The case study demonstrates the utility of using more advanced GA techniques to obtain acceptable solution quality as problem diffic
Utilizing meta-heuristic global optimization algorithms in gas turbine aero-engines modelling and control problems is proposed over the past two decades as a methodological approach. The purpose of the review is to es...
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Utilizing meta-heuristic global optimization algorithms in gas turbine aero-engines modelling and control problems is proposed over the past two decades as a methodological approach. The purpose of the review is to establish evident shortcomings of these approaches and to identify the remaining research challenges. These challenges need to be addressed to enable the novel, cost-effective techniques to be adopted by aero-engine designers. First, the benefits of global optimization algorithms are stated in terms of philosophy and the nature of different types of these methods. Then, a historical coverage is given for the applications of different optimization techniques applied in different aspects of gas turbine modelling, controller design, and tuning fields. The main challenges for the application of meta-heuristic global optimization algorithms in new advanced engine designs are presented. To deal with these challenges, two efficient optimization algorithms, competent genetic algorithm in single objective feature and aggregative gradient-based algorithm in multi-objective feature are proposed and applied in a turbojet engine controller gain-tuning problem as a case study. A comparison with the publicly available results show that optimization time and convergence indices will be enhanced noticeably. Based on this comparison and analysis, the potential solutions for the remaining research challenges for application to aerospace engineering problems in the future include the implementation of enhanced and modified optimization algorithms and hybrid optimization algorithms in order to achieve optimal results for the advanced engine modelling and controller design procedure with affordable computational effort.
This paper describes the application of biologically-inspired algorithms and concepts to the design of wideband antenna arrays. In particular, we address two specific design problems. The first involves the design of ...
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ISBN:
(纸本)9780819466853
This paper describes the application of biologically-inspired algorithms and concepts to the design of wideband antenna arrays. In particular, we address two specific design problems. The first involves the design of a constrained-feed network for a Rotman-lens beamformer. We implemented two evolutionary optimization (EO) approaches, namely a simple geneticalgorithm (SGA) and a competent genetic algorithm. We conducted simulations based on experimental data, which effectively demonstrate that the competent GA outperforms the SGA (i.e., finds a better design solution) as the objective function becomes less specific and more "general." The second design problem involves the implementation of polyomino-shaped subarrays for sidelobe suppression of large, wideband planar arrays. We use a modified screen-saver code to generate random polyomino filings. A separate code assigns array values to each element of the tiling (i.e., amplitude, phase, time delay, etc.) and computes the corresponding far-field radiation pattern. In order to conduct a statistical analysis of pattern characteristics vs. tiling geometry, we needed a way to measure the "similarity" between two arbitrary filings to ensure that our sampling of the tiling space was somewhat uniformly distributed We ultimately borrowed a concept from neural network theory, which we refer to as the "dot-product metric," to effectively categorize filings based on their degree of similarity.
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