Expedited Globalized Antenna Optimization by Principal Components and Variable-Fidelity EM Simulations: Application to Microstrip Antenna Design

Publisher's version (útgefin grein)Parameter optimization, also referred to as design closure, is imperative in the development of modern antennas. Theoretical considerations along with rough dimension adjustment through supervised parameter sweeping can only yield initial designs that need to be further tuned to boost the antenna performance. The major challenges include handling of multi-dimensional parameter spaces while accounting for several objectives and constraints. Due to complexity of modern antenna topologies, parameter interactions are often involved, leading to multiple local optima as well as difficulties in identifying decent initial designs that can be improved using local procedures. In such cases, global search is required, which is an expensive endeavor, especially if full-wave electromagnetic (EM) analysis is employed for antenna evaluation. This paper proposes a novel technique accommodating the search space exploration using local kriging surrogates and local improvement by means of trust-region gradient search. Computational efficiency of the process is achieved by constructing the metamodels over appropriately defined affine subspaces and incorporation of coarse-mesh EM simulations at the exploratory stages of the optimization process. The resulting framework enables nearly global search capabilities at the costs comparable to conventional gradient-based local optimization. This is demonstrated using two antenna examples and comparative studies involving multiple-start local tuning.This work is partially supported by the Icelandic Centre for Research (RANNIS) Grant 174573051 and by National Science Centre of Poland Grant 2018/31/B/ST7/02369."Peer Reviewed

A New Optimization Algorithm Based on the Fungi Kingdom Expansion Behavior for Antenna Applications

This paper presents a new optimization algorithm based on the behavior of the fungi kingdom expansion (FKE) to optimize the radiation pattern of the array antenna. The immobile mass expansion of the fungi is mimicked in this work as a chaotic behavior with a sinusoidal map function, while the mobile mass expansion is realized by a linear function. In addition, the random germination of the spores is utilized for randomly distributing the variables that are far away from the best solution. The proposed FKE algorithm is applied to optimize the radiation pattern of the antenna array, and then its performance is compared with that of some well-known algorithms. The MATLAB simulation results verify the superiority of the proposed algorithm in solving 20-element antenna array problems such as sidelobe reduction with sidelobe ratio (SLR = 25.6 dB), flat-top pattern with SLR = 23.5 dB, rectangular pattern with SLR = 19 dB, and anti-jamming systems. The algorithm also results in a 100% success rate for all of the mentioned antenna array problems

Modelling, Monitoring, Control and Optimization for Complex Industrial Processes

This reprint includes 22 research papers and an editorial, collected from the Special Issue "Modelling, Monitoring, Control and Optimization for Complex Industrial Processes", highlighting recent research advances and emerging research directions in complex industrial processes. This reprint aims to promote the research field and benefit the readers from both academic communities and industrial sectors

Bio-Inspired Robotics

Modern robotic technologies have enabled robots to operate in a variety of unstructured and dynamically-changing environments, in addition to traditional structured environments. Robots have, thus, become an important element in our everyday lives. One key approach to develop such intelligent and autonomous robots is to draw inspiration from biological systems. Biological structure, mechanisms, and underlying principles have the potential to provide new ideas to support the improvement of conventional robotic designs and control. Such biological principles usually originate from animal or even plant models, for robots, which can sense, think, walk, swim, crawl, jump or even fly. Thus, it is believed that these bio-inspired methods are becoming increasingly important in the face of complex applications. Bio-inspired robotics is leading to the study of innovative structures and computing with sensory–motor coordination and learning to achieve intelligence, flexibility, stability, and adaptation for emergent robotic applications, such as manipulation, learning, and control. This Special Issue invites original papers of innovative ideas and concepts, new discoveries and improvements, and novel applications and business models relevant to the selected topics of ``Bio-Inspired Robotics''. Bio-Inspired Robotics is a broad topic and an ongoing expanding field. This Special Issue collates 30 papers that address some of the important challenges and opportunities in this broad and expanding field