Multi-objective Antenna Research Articles

Multi-Objective Antenna Design Based on BP Neural Network Surrogate Model Optimized by Improved Sparrow Search Algorithm

To solve the time-consuming, laborious, and inefficient problems of traditional methods using classical optimization algorithms combined with electromagnetic simulation software to design antennas, an efficient design method of the multi-objective antenna is proposed based on the multi-strategy improved sparrow search algorithm (MISSA) to optimize a BP neural network. Three strategies, namely Bernoulli chaotic mapping, inertial weights, and t-distribution, are introduced into the sparrow search algorithm to improve its convergent speed and accuracy. Using the Bernoulli chaotic map to process the population of sparrows to enhance its population richness, the weight is introduced into the updated position of the sparrow to improve its search ability. The adaptive t-distribution is used to interfere and mutate some individual sparrows to make the algorithm reach the optimal solution more quickly. The initial parameters of the BP neural network were optimized using the improved sparrow search algorithm to obtain the optimized MISSA-BP antenna surrogate model. This model is combined with multi-objective particle swarm optimization (MOPSO) to solve the design problem of the multi-objective antenna and verified by a triple-frequency antenna. The simulated results show that this method can predict the performance of the antennas more accurately and can also design the multi-objective antenna that meets the requirements. The practicality of the method is further verified by producing a real antenna.

Multistage Collaborative Machine Learning and its Application to Antenna Modeling and Optimization

A multistage collaborative machine learning (MS-CoML) method that can be applied to efficient multiobjective antenna modeling and optimization is proposed. Machine learning methods, including single-output Gaussian process regression (SOGPR) and symmetric and asymmetric multioutput GPR (MOGPR) methods, are introduced to collaboratively build highly accurate multitask surrogate models for antennas. Variable-fidelity electromagnetic (EM) models are simulated, with their responses utilized to build separate MOGPR surrogate models. By combining the three machine-learning methods in a multistage framework, mappings between the same and different responses of the EM models with variable fidelity are learned, therein helping to substantially reduce the computational effort under a negligible loss of predictive power. Three antenna designs aiming at single-band, broadband, and multiband applications are selected as examples. And, for illustrating the applicability and superiority of the proposed MS-CoML method, a reference point-based multiobjective antenna optimization algorithm is used to optimize these three antennas. Simulation results show that using the MS-CoML method can significantly reduce the total optimization time without compromising modeling accuracy and optimized performance.

Rapid multi-objective optimization of antennas using nested kriging surrogates and single-fidelity EM simulation models

PurposeThis study aims to propose a computationally efficient framework for multi-objective optimization (MO) of antennas involving nested kriging modeling technology. The technique is demonstrated through a two-objective optimization of a planar Yagi antenna and three-objective design of a compact wideband antenna.Design/methodology/approachThe keystone of the proposed approach is the usage of recently introduced nested kriging modeling for identifying the design space region containing the Pareto front and constructing fast surrogate model for the MO algorithm. Surrogate-assisted design refinement is applied to improve the accuracy of Pareto set determination. Consequently, the Pareto set is obtained cost-efficiently, even though the optimization process uses solely high-fidelity electromagnetic (EM) analysis.FindingsThe optimization cost is dramatically reduced for the proposed framework as compared to other state-of-the-art frameworks. The initial Pareto set is identified more precisely (its span is wider and of better quality), which is a result of a considerably smaller domain of the nested kriging model and better predictive power of the surrogate.Research limitations/implicationsThe proposed technique can be generalized to accommodate low- and high-fidelity EM simulations in a straightforward manner. The future work will incorporate variable-fidelity simulations to further reduce the cost of the training data acquisition.Originality/valueThe fast MO optimization procedure with the use of the nested kriging modeling technology for approximation of the Pareto set has been proposed and its superiority over state-of-the-art surrogate-assisted procedures has been proved. To the best of the authors’ knowledge, this approach to multi-objective antenna optimization is novel and enables obtaining optimal designs cost-effectively even in relatively high-dimensional spaces (considering typical antenna design setups) within wide parameter ranges.

Multi-objective Antenna Selection in a Full Duplex Base Station

The use of full-duplex (FD) communication systems is a new way to increase spectral efficiency. For this reason, it has received serious attention in the new generation of wireless communication systems. The main challenge of FD systems is self-interference that needs to be reduced appropriately. In this paper, we have considered an FD multi-antenna base station in a cellular network and we have used the antenna selection technique to resolve the self-interference issue. We have also provided a new criterion to select an appropriate antenna. In this new criterion, the antenna selection is modeled as a multi-objective optimization problem. Here, the antennas which simultaneously minimizes interference channel gain and maximizes uplink (UL) and downlink (DL) channel gains are selected for transmission and reception. The base station has to perform well in both the UL and DL and reduce the self-interference simultaneously. Therefore, the multi-objective criterion has a better performance than the single-objective criterion. Although, in conventional antenna selection with the single-objective criterion, only the function of the UL and DL channels or the interference channel is considered. Finally, the simulations show that the new criterion has a higher throughput rate than the other conventional single-objective antenna selection techniques.

Low-Cost Multi-Objective Optimization of Multiparameter Antenna Structures Based on the l1 Optimization BPNN Surrogate Model

The development of modern wireless communication systems not only requires the antenna to be lightweight, low cost, easy to manufacture and easy to integrate but also imposes requirements on the miniaturization, wideband, and multiband design of the antenna. Therefore, designing an antenna that quickly and effectively meets multiple performance requirements is of great significance. To solve the problem of the large computational cost of traditional multi-objective antenna design methods, this paper proposes a backpropagation neural network surrogate model based on l1 optimization (l1-BPNN). The l1 optimization method tends to punish larger weight values and select smaller weight values so as to preserve a small amount of important weights and reset relatively unimportant weights to zero. By using l1 optimization method, the network mapping structure can be automatically adjusted to achieve the most suitable and compact structure of the surrogate model. Furthermore, for multi-parameter antenna design problems, a fast multi-objective optimization framework is constructed using the proposed l1-BPNN as a surrogate model. The framework is illustrated using a miniaturized multiband antenna design case, and a comparison with previously published methods, as well as numerical validation, is also provided.

Fast Multi-Objective Antenna Optimization Based on RBF Neural Network Surrogate Model Optimized by Improved PSO Algorithm

In this paper, a radial basis function neural network (RBFNN) surrogate model optimized by an improved particle swarm optimization (PSO) algorithm is developed to reduce the computation cost of traditional antenna design methods which rely on high-fidelity electromagnetic (EM) simulations. Considering parameters adjustment and update mechanism simultaneously, two modifications are proposed in this improved PSO. First, time-varying learning factors are designed to balance exploration and exploitation ability of particles in the search space. Second, the local best information is added to the updating process of particles except for personal and global best information for better population diversity. The improved PSO is applied to train RBFNN for determining optimal network parameters. As a result, the constructed improved PSO-RBFNN model can be used as a surrogate model for antenna performance prediction with better network generalization capability. By integrating the improved PSO-RBFNN surrogate model with multi-objective evolutionary algorithms (MOEAs), a fast multi-objective antenna optimization framework for multi-parameter antenna structures is then established. Finally, a Pareto-optimal planar miniaturized multiband antenna design is presented, demonstrating that the proposed model provides better prediction performance and considerable computational savings compared to those previously published approaches.

Fast Multi-Objective Optimization of Multi-Parameter Antenna Structures Based on Improved BPNN Surrogate Model

In this paper, a surrogate model based on a sparsely connected back propagation neural networks (SC-BPNN) is proposed to reduce the large computational cost of conventional multi-objective antenna optimization problems. In this model, the connection parameters and network structure can be adaptively tuned by a hybrid real-binary particle swarm optimization (HPSO) algorithm for better network global optimization capability. Also, a time-varying transfer function is introduced to improve the problem of easily trapping into local optimum and to accelerate network convergence. Further, a fast multi-objective optimization framework based on the proposed SC-BPNN is established for multi-parameter antenna structures. Finally, a Pareto-optimal planar miniaturized multiband antenna design is presented, indicating that the proposed model predicts antenna performance more accurately and saves considerable computational cost compared to those previously published approaches.

Patch size setup and performance/cost trade-offs in multi-objective EM-driven antenna optimization using sequential domain patching

PurposeThis paper aims to assess control parameter setup and its effect on computational cost and performance of deterministic procedures for multi-objective design optimization of expensive simulation models of antenna structures.Design/methodology/approachA deterministic algorithm for cost-efficient multi-objective optimization of antenna structures has been assessed. The algorithm constructs a patch connecting extreme Pareto-optimal designs (obtained by means of separate single-objective optimization runs). Its performance (both cost- and quality-wise) depends on the dimensions of the so-called patch, an elementary region being relocated in the course of the optimization process. The cost/performance trade-offs are studied using two examples of ultra-wideband antenna structures and the optimization results are compared to draw conclusions concerning the algorithm robustness and determine the most advantageous control parameter setups.FindingsThe obtained results indicate that the investigated algorithm is very robust, i.e. its performance is weakly dependent on the control parameters setup. At the same time, it is found that the most suitable setups are those that ensure low computational cost, specifically non-uniform ones generated on the basis of sensitivity analysis.Research limitations/implicationsThe study provides recommendations for control parameter setup of deterministic multi-objective optimization procedure for computationally efficient design of antenna structures. This is the first study of this kind for this particular design procedure, which confirms its robustness and determines the most suitable arrangement of the control parameters. Consequently, the presented results permit full automation of the surrogate-assisted multi-objective antenna optimization process while ensuring its lowest possible computational cost.Originality/valueThe work is the first comprehensive validation of the sequential domain patching algorithm under various scenarios of its control parameter setup. The considered design procedure along with the recommended parameter arrangement is a robust and computationally efficient tool for fully automated multi-objective optimization of expensive simulation models of contemporary antenna structures.

GUI design exploration software for microwave antennas

Abstract Optimizers in commercial electromagnetic (EM) simulation software packages are the main tools for performing antenna design exploration today. However, these general purpose optimizers are facing challenges in optimization efficiency, supported optimization types and usability for antenna experts without deep knowledge on optimization. Aiming to fill the gaps, a new antenna design exploration tool, called Antenna Design Explorer (ADE), is presented in this paper. The key features are: (1) State-of-the-art antenna design exploration methods are selected and embedded, addressing efficient antenna optimization (critical but unable to be solved by existing tools) and multiobjective antenna optimization (not available in most existing tools); (2) Human-computer interaction for the targeted problem is studied, addressing various usability issues for antenna design engineers, such as automatic algorithmic parameter setting and interactive stopping criteria; (3) Compatibility with existing tools is studied and ADE is able to co-work with existing EM simulators and optimizers, combining advantages. A case study verifies the advantages of ADE. Highlights A new antenna design exploration tool, called Antenna Design Explorer (ADE), is presented in this paper. State-of-the-art antenna design exploration methods are selected and embedded, addressing efficient antenna optimization (critical but difficult to be solved by existing tools) and multiobjective antenna optimization (not available in most existing tools). Human-computer interaction for the targeted problem is studied, addressing various usability issues for antenna design engineers. Compatibility with existing tools is studied and ADE is able to co-work with existing EM simulators and optimizers, combining advantages.

Rapid Multiobjective Antenna Design Using Point-By-Point Pareto Set Identification and Local Surrogate Models

Antenna design is inherently a multicriterial problem. Determination of the best possible tradeoffs between conflicting objectives (a so-called Pareto front), such as reflection response, gain, and antenna size, is indispensable from the designer’s point of view, yet challenging when high-fidelity electromagnetic (EM) simulations are utilized for performance evaluation. Here, a novel and computationally efficient methodology for multiobjective optimization of antenna structures is presented. In our approach, the tradeoff designs are obtained by moving along the Pareto front and identifying the subsequent Pareto-optimal solutions using surrogate-based optimization techniques. Computational efficiency of the process is achieved by employing coarse-discretization EM simulations and local response surface approximation (RSA) models. The proposed approach is demonstrated using a compact ultrawideband (UWB) monopole antenna with a representation of the Pareto front obtained at the cost corresponding to just a few dozen of evaluations of the high-fidelity EM antenna model. Experimental validation is also provided.

Scalability of surrogate-assisted multi-objective optimization of antenna structures exploiting variable-fidelity electromagnetic simulation models

Multi-objective optimization of antenna structures is a challenging task owing to the high computational cost of evaluating the design objectives as well as the large number of adjustable parameters. Design speed-up can be achieved by means of surrogate-based optimization techniques. In particular, a combination of variable-fidelity electromagnetic (EM) simulations, design space reduction techniques, response surface approximation models and design refinement methods permits identification of the Pareto-optimal set of designs within a reasonable timeframe. Here, a study concerning the scalability of surrogate-assisted multi-objective antenna design is carried out based on a set of benchmark problems, with the dimensionality of the design space ranging from six to 24 and a CPU cost of the EM antenna model from 10 to 20 min per simulation. Numerical results indicate that the computational overhead of the design process increases more or less quadratically with the number of adjustable geometric parameters of the antenna structure at hand, which is a promising result from the point of view of handling even more complex problems.

Multiobjective Antenna Design By Means of Sequential Domain Patching

A simple yet robust methodology for rapid multiobjective design optimization of antenna structures has been presented. The key component of our approach is sequential domain patching of the design space, which is a stencil-based search that aims at creating a path that connects the extreme Pareto-optimal designs, obtained by means of single-objective optimization runs. The patching process yields the initial approximation of the Pareto set representing the best possible tradeoffs between conflicting design objectives. It is realized-for the sake of computational efficiency-at the level of coarse-discretization electromagnetic (EM) simulation model. The final Pareto front is subsequently obtained using surrogate-assisted optimization where the EM simulation data acquired at the initial design stage is reused.

Efficient Multi-Objective Simulation-Driven Antenna Design Using Co-Kriging

A methodology for fast multi-objective antenna optimization is presented. Our approach is based on response surface approximation (RSA) modeling and variable-fidelity electromagnetic (EM) simulations. In the design process, a computationally cheap RSA surrogate model constructed from sampled coarse-discretization EM antenna simulations is optimized using a multi-objective evolutionary algorithm. The initially determined Pareto optimal set representing the best possible trade-offs between conflicting design objectives is then iteratively refined. In each iteration, a limited number of high-fidelity EM model responses are incorporated into the RSA model using co-kriging. The enhanced RSA model is subsequently re-optimized to yield the refined Pareto set. Combination of low- and high-fidelity simulations as well as co-kriging results in the low overall optimization cost. The proposed approach is validated using two UWB antenna examples.

Low-cost EM-simulation-driven Multi-objective Optimization of Antennas

A surrogate-based method for efficient multi-objective antenna optimization is presented. Our technique exploits response surface approximation (RSA) model constructed from sampled low-fidelity antenna model (here, obtained through coarse-discretization EM simulation). The RSA model enables fast determination of the best available trade-offs between conflicting design goals. A low-cost RSA model construction is possible through initial reduction of the design space. Optimization of the RSA model has been carried out using a multi-objective evolutionary algorithm (MOEA). Additional response correction techniques have been subsequently applied to improve selected designs at the high-fidelity EM antenna model level. The refined designs constitute the final Pareto set representation. The proposed approach has been validated using an ultra-wideband (UWB) monocone and a planar Yagi-Uda antenna.

Many-Objective Optimization of Antenna Arrays Using an Improved Multiple-Single-Objective Pareto Sampling Algorithm

Pareto-based multiobjective evolutionary algorithms are recognized as the standards for solving multiobjective antenna design problems. However, when the number of objectives exceeds three, these algorithms always exhibit deficiencies in searching the Pareto front. To eliminate these deficiencies, several algorithms, such as Hyper-volume Estimation (HypE) algorithm and Multiple-Single-Objective Pareto Sampling (MSOPS) algorithm, are introduced. In this letter, an improved MSOPS algorithm is proposed. The numerical results on many-objective optimal designs of a linear antenna array and a Yagi-Uda array have demonstrated that the proposed algorithm outperforms its ancestor, NSGA II, and HypE as well.