Efficient design optimization of high-performance MEMS based on a surrogate-assisted self-adaptive differential evolution

High-performance microelectromechanical systems (MEMS) are playing a critical role in modern engineering systems. Due to computationally expensive numerical analysis and stringent design specifications nowadays, both the optimization efficiency and quality of design solutions become challenges for available MEMS shape optimization methods. In this paper, a new method, called self-adaptive surrogate model-assisted differential evolution for MEMS optimization (ASDEMO), is presented to address these challenges. The main innovation of ASDEMO is a hybrid differential evolution mutation strategy combination and its self-adaptive adoption mechanism, which are proposed for online surrogate model-assisted MEMS optimization. The performance of ASDEMO is demonstrated by a high-performance electro-thermo-elastic micro-actuator, a high-performance corrugated membrane microactuator, and a highly multimodal mathematical benchmark problem. Comparisons with state-of-the-art methods verify the advantages of ASDEMO in terms of efficiency and optimization ability

Multiphysics field analysis and evolutionary optimization: Design of an electro-thermo-elastic microactuator

In the paper, the optimization of an electro-thermo-elastic microactuator is proposed. In particular, the maximum temperature of the actuator is to be minimized, while the total displacement is to be maximized. For solving this problem, the Adaptive Gaussian Process-Assisted Differential Evolution AGDEMO method is applied

A Parallel Surrogate Model Assisted Evolutionary Algorithm for Electromagnetic Design Optimization

Optimization efficiency is a major challenge for electromagnetic (EM) device, circuit, and machine design. Although both surrogate model-assisted evolutionary algorithms (SAEAs) and parallel computing are playing important roles in addressing this challenge, there is little research that investigates their integration to benefit from both techniques. In this paper, a new method, called parallel SAEA for electromagnetic design (PSAED), is proposed. A state-of-the-art SAEA framework, surrogate model-aware evolutionary search, is used as the foundation of PSAED. Considering the landscape characteristics of EM design problems, three differential evolution mutation operators are selected and organized in a particular way. A new SAEA framework is then proposed to make use of the selected mutation operators in a parallel computing environment. PSAED is tested by a micromirror and a dielectric resonator antenna as well as four mathematical benchmark problems of various complexity. Comparisons with state-of-the-art methods verify the advantages of PSAED in terms of efficiency and optimization capacity

Global Optimization of Microwave Filters Based on a Surrogate Model Assisted Evolutionary Algorithm

Local optimization is a routine approach for full-wave optimization of microwave filters. For filter optimization problems with numerous local optima or where the initial design is not near to the optimal region, the success rate of the routine method may not be high. Traditional global optimization techniques have a high success rate for such problems, but are often prohibitively computationally expensive considering the cost of full-wave electromagnetic simulations. To address the above challenge, a new method, called surrogate model-assisted evolutionary algorithm for filter optimization (SMEAFO), is proposed. In SMEAFO, considering the characteristics of filter design landscapes, Gaussian process surrogate modeling, differential evolution operators, and Gaussian local search are organized in a particular way to balance the exploration ability and the surrogate model quality, so as to obtain high-quality results in an efficient manner. The performance of SMEAFO is demonstrated by two real-world design cases (a waveguide filter and a microstrip filter), which do not appear to be solvable by popular local optimization techniques. Experiments show that SMEAFO obtains high-quality designs comparable with global optimization techniques but within a reasonable amount of time. Moreover, SMEAFO is not restricted by certain types of filters or responses. The SMEAFO-based filter design optimization tool can be downloaded from http://fde.cadescenter.com

Reflecting/Absorbing Dual-Mode Textile Metasurface with AI-Driven Parametric Studies

This paper presents a textile-based reflecting/absorbing dual-mode metasurface, and emphasizes the role of AI-driven parametric studies in the designing process. The proposed textile metasurface can achieve a reflecting mode with the zero-degree refection phase centre at 2.4 GHz, and an absorbing mode at the same resonance frequency. The absorption and reflection band of the design are centered at the same frequency by applying a state-of-the-art AI-driven antenna design technique, self-adaptive Bayesian neural network surrogate model-assisted differential evolution for antenna optimization (SB-SADEA) method. The proposed design can also achieve polarization insensitivity and a certain level of incident angle insensitivity. The fabricated prototype of the design achieves a maximal absorption rate of 99.8% and maintains an absorption over 90% in the frequency range of 2.39 to 2.43 GHz. Moreover, a textile linear polarized monopole antenna was fabricated and tested along with the reflection metasurface. A 5 dB realized gain enhancement can be achieved with the metasurface applied. Both simulations and measurements verify the effectiveness of the proposed dual-mode textile metasurface design

Global Optimization of Microwave Filters Based on a Surrogate Model Assisted Evolutionary Algorithm

Local optimization is a routine approach for full-wave optimization of microwave filters. For filter optimization problems with numerous local optima or where the initial design is not near to the optimal region, the success rate of the routine method may not be high. Traditional global optimization techniques have a high success rate for such problems, but are often prohibitively computationally expensive considering the cost of full-wave electromagnetic simulations. To address the above challenge, a new method, called surrogate model-assisted evolutionary algorithm for filter optimization (SMEAFO), is proposed. In SMEAFO, considering the characteristics of filter design landscapes, Gaussian process surrogate modeling, differential evolution operators, and Gaussian local search are organized in a particular way to balance the exploration ability and the surrogate model quality, so as to obtain high-quality results in an efficient manner. The performance of SMEAFO is demonstrated by two real-world design cases (a waveguide filter and a microstrip filter), which do not appear to be solvable by popular local optimization techniques. Experiments show that SMEAFO obtains high-quality designs comparable with global optimization techniques but within a reasonable amount of time. Moreover, SMEAFO is not restricted by certain types of filters or responses. The SMEAFO-based filter design optimization tool can be downloaded from http://fde.cadescenter.com

An Efficient Method for Antenna Design Based on a Self-Adaptive Bayesian Neural Network-Assisted Global Optimization Technique

Gaussian process (GP) is a very popular machine learning method for online surrogate-model-assisted antenna design optimization. Despite many successes, two improvements are important for the GP-based antenna global optimization methods, including: 1) the convergence speed (i.e., the number of necessary electromagnetic (EM) simulations to obtain a high-performance design) and 2) the GP model training cost when there are several tens of design variables and/or specifications. In both aspects, the state-of-the-art GP-based methods show practical but not desirable performance. Therefore, a new method, called the self-adaptive Bayesian neural network surrogate-model-assisted differential evolution (DE) for antenna optimization (SB-SADEA), is presented in this article. The key innovations include: 1) the introduction of the Bayesian neural network (BNN)-based antenna surrogate modeling method into this research area, replacing GP modeling, and 2) a bespoke self-adaptive lower confidence bound (LCB) method for antenna design landscape making use of the BNN-based antenna surrogate model. The performance of SB-SADEA is demonstrated by two challenging design cases, showing considerable improvement in terms of both convergence speed and machine learning cost compared with the state-of-the-art GP-based antenna global optimization methods

Parasitic Element Based Frequency Reconfigurable Antenna with Dual Wideband Characteristics for Wireless Applications

A Microstrip Frequency Reconfigurable circular patch slot antenna for switchable Bluetooth, WiMAX, WLAN, and satellite communication applications is analyzed and presented in this work. The optimized overall size of 47 mm x40 mmx1.6 mm is utilized in the design, and which can cover wide range of frequencies below 10 GHz. In the initial phase, different monopole antennas are designed with various shapes of same size and later parasitic patch elements has been added to those monopole antennas. The circular monopole driven element and parasitic element are connected with a PIN diode, and which reinforced in achieving frequency reconfigurability. The proposed antenna is resonating at various frequencies of 2.4 GHz, 4 GHz, and 8.4 GHz when the diode in ON condition and resonating at 3 GHz, 5.4 GHz, and 8.4 GHz when the diode is in OFF condition. The performance of the designed antenna prototype is scaled and differentiated with the results of simulation and found good matching with respect to performance characteristics

An efficient method for antenna design based on a self-adaptive bayesian neural network assisted global optimization technique

Gaussian process (GP) is a very popular machine learning method for online surrogate model-assisted antenna design optimization. Despite many successes, two improvements are important for GP-based antenna global optimization methods, including (1) the convergence speed (i.e., the number of necessary electromagnetic simulations to obtain a high-performance design), and (2) the GP model training cost when there are several tens of design variables and/or specifications. In both aspects, state-of-the-art GP-based methods show practical but not desirable performance. Therefore, a new method, called self-adaptive Bayesian neural network surrogate model-assisted differential evolution for antenna optimization (SB-SADEA), is presented in this paper. The key innovations include: (1) The introduction of the Bayesian neural network (BNN)-based antenna surrogate modeling method into this research area, replacing GP modeling, and (2) a bespoke self-adaptive lower confidence bound method for antenna design landscape making use of the BNN-based antenna surrogate model. The performance of SB-SADEA is demonstrated by two challenging design cases, showing considerable improvement in terms of both convergence speed and machine learning cost compared to state-of-the-art GP-based antenna global optimization methods