Design and Optimization of 3-D Frequency-Selective Surfaces Based on a Multiobjective Lazy Ant Colony Optimization Algorithm

Zhu, Danny; Werner, Pingjuan L.; Werner, Douglas H.

doi:10.1109/tap.2017.2766660

Cited by 114 publications

(43 citation statements)

References 49 publications

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“…Genetic Algorithm is an evolutionary algorithm developed on the principles of genetics [5]. It is a nature inspired algorithm [6].…”

Section: Genetic Algorithmmentioning

confidence: 99%

Review on Linear Array Antenna with Minimum Side Lobe Level Using Genetic Algorithm

Sharma¹,

Patidar²

2018

IJTSRD

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Antenna array is formed by assembly of radiating elements in an electrical or geometrical configuration. In most cases the elements are identical. In this paper proposed a very simple and powerful method for the synthesis of linear array antenna and GA. This method reduced the desired level of side lobe level (SLL) as well as to steer the main beam at different different angle. A new method for adaptive beam forming for a linear antenna arrays using genetic algorithm (GA) are also proposed.

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“…Genetic Algorithm is an evolutionary algorithm developed on the principles of genetics [5]. It is a nature inspired algorithm [6].…”

Section: Genetic Algorithmmentioning

confidence: 99%

Review on Linear Array Antenna with Minimum Side Lobe Level Using Genetic Algorithm

Sharma¹,

Patidar²

2018

IJTSRD

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“…Ant colony optimization has been recognized as an efficient and robust optimization technique and has been used frequently in different applications [12]- [15]. Studies using ACO to solve the global path planning problem have been carried out in recent years.…”

Section: Introductionmentioning

confidence: 99%

A New Robot Navigation Algorithm Based on a Double-Layer Ant Algorithm and Trajectory Optimization

Yang

Miao

et al. 2019

IEEE Trans. Ind. Electron.

130

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This paper presents an efficient double-layer ant colony algorithm, called DL-ACO, for autonomous robot navigation. This DL-ACO consists of two ant colony algorithms which run independently and successively. First, a parallel elite ant colony optimization (PEACO) method is proposed to generate an initial collision-free path in a complex map, and then we apply a path improvement algorithm called turning point optimization algorithm (TPOA), in which the initial path is optimized in terms of length, smoothness and safety. Besides, a piecewise B-spline path smoother is presented for easier tracking control of the mobile robot. Our method is tested by simulations and compared with other path planning algorithms. The results show that our method can generate better collision-free path efficiently and consistently, which demonstrates the effectiveness of the proposed algorithm. Furthermore, its performance is validated by experiments in indoor and outdoor environments.

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“…Yet, the computational overhead of EM-driven design may prove unacceptable even in the case of local optimization, 12 let alone global search procedures, 13 typically involving population-based metaheuristics. [14][15][16] Incorporation of adjoint sensitivities into gradient-based procedures 17 is one way of mitigating the high cost issue. Other approaches include algorithmic acceleration of conventional routines (eg, sparse sensitivity updates 18,19 ), or surrogate-based optimization (SBO), 20 founded on shifting the computational burden into the fast replacement model (surrogate).…”

Section: Introductionmentioning

confidence: 99%

Cost‐efficient performance‐driven modelling of multi‐band antennas by variable‐fidelity electromagnetic simulations and customized space mapping

Koziel

Pietrenko‐Dabrowska

2020

Int J Numerical Modelling

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Electromagnetic (EM) simulations have become an indispensable tool in the design of contemporary antennas. EM-driven tasks, for example, parametric optimization, entail considerable computational efforts, which may be reduced by employing surrogate models. Yet, data-driven modelling of antenna characteristics is largely hindered by the curse of dimensionality. This may be addressed using the recently reported domain-confinement techniques, especially the nested-kriging framework, which permits rendering of reliable surrogates over wide ranges of antenna parameters while greatly reducing the computational overhead of training data acquisition. Focused on modelling of multi-band antennas, this paper attempts to reduce the cost of surrogate construction even further by incorporating variable-fidelity simulations into the nested kriging. The principal challenge being design-dependent frequency shifts between the models of various fidelities is handled through the development of a customized frequency scaling and output space mapping. Validation is carried out using a dual-band dipole antenna modeled over broad ranges of operating conditions. A small training data set is sufficient to secure the predictive power comparable to that of the nested kriging model set up using solely high-fidelity data, and by far exceeding the accuracy of conventional surrogates. Application examples for antenna optimization and experimental verification of the selected designs are also provided.

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Design and Optimization of 3-D Frequency-Selective Surfaces Based on a Multiobjective Lazy Ant Colony Optimization Algorithm

Cited by 114 publications

References 49 publications

Review on Linear Array Antenna with Minimum Side Lobe Level Using Genetic Algorithm

Review on Linear Array Antenna with Minimum Side Lobe Level Using Genetic Algorithm

A New Robot Navigation Algorithm Based on a Double-Layer Ant Algorithm and Trajectory Optimization

Cost‐efficient performance‐driven modelling of multi‐band antennas by variable‐fidelity electromagnetic simulations and customized space mapping

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