Feature selection is a critical and prominent task in machine learning. To reduce the dimension of the feature set while maintaining the accuracy of the performance is the main aim of the feature selection problem. Various methods have been developed to classify the datasets. However, metaheuristic algorithms have achieved great attention in solving numerous optimization problem. Therefore, this paper presents an extensive literature review on solving feature selection problem using metaheuristic algorithms which are developed in the ten years (2009-2019). Further, metaheuristic algorithms have been classified into four categories based on their behaviour. Moreover, a categorical list of more than a hundred metaheuristic algorithms is presented. To solve the feature selection problem, only binary variants of metaheuristic algorithms have been reviewed and corresponding to their categories, a detailed description of them explained. The metaheuristic algorithms in solving feature selection problem are given with their binary classification, name of the classifier used, datasets and the evaluation metrics. After reviewing the papers, challenges and issues are also identified in obtaining the best feature subset using different metaheuristic algorithms. Finally, some research gaps are also highlighted for the researchers who want to pursue their research in developing or modifying metaheuristic algorithms for classification. For an application, a case study is presented in which datasets are adopted from the UCI repository and numerous metaheuristic algorithms are employed to obtain the optimal feature subset.

An inkjet-printed reconfigurable multiband antenna printed on a low-cost commercial photo paper substrate using two p-i-n diodes is proposed for the first time. A main radiator is designed to operate at 3.4 GHz band, whereas L-shaped and U-shaped radiating elements are designed to operate at 1.9 and 2.4 GHz, respectively. The antenna has a size of 30 × 40 × 0.44 mm ³ and can cover most of the mobile and wireless bands between 1.5 and 4 GHz. A prototype of the proposed antenna is fabricated and measured. Measured and simulated results of S11, radiation pattern, gain, and efficiency are in good agreement. Measured results show that the antenna has a gain and efficiency of 2 dBi and 50%, respectively. A parametric study is proposed as a guidance to design the antenna for operating in other bands between 1.5 and 4 GHz.

A flexible, wideband, and screen-printed antenna is proposed. The antenna is coplanar-waveguide (CPW) and composed of two inverted L-shape elements, a matching stub, and a defected ground structure (DGS), with a total area of 55 40 0.125 mm3. Studies show that the DGS can significantly increase the antenna bandwidth from 30% to 119% without sacrificing the size. The antenna has a wide measured bandwidth of 1.77 - 6.95 GHz, and a measured peak gain and efficiency in the ranges of 2.5-5.9 dBi and 60%-90%, respectively. Furthermore, both simulation and measurement confirm that the performance of the antenna is relatively stable under the bending conditions with various curvature radii.

As optimization algorithms have a great power to solve nonlinear, complex, and hard optimization problems, nature-inspired algorithms have been applied extensively in distinct fields in order to solve real life optimization cases. In this paper, modifications for the recently proposed Gaining-Sharing-Knowledge based algorithm (GSK) are presented for enhancing its performance. Gaining-Sharing-Knowledge algorithm is considered as a perfect example of modern nature-inspired algorithm that considered the human life behavior as a source of inspiration in order to solve optimization problems. GSK algorithm mimics the naturalistic phenomenon of gaining knowledge and sharing knowledge back in human being through two major phasis: junior phase and senior phase. The junior phase is based on generating a set of initial solutions (juniors) to start the search process. Subsequently, all individuals move to the senior phase through the interaction with the surrounding environment and the cooperation with other individuals throughout the search process. The main idea in this presented work is extending and improving the original GSK algorithm through proposing a new idea for adaptively obtain the values for the two significant control parameters in GSK: the knowledge factor and the knowledge ratio. Those two significant parameters have the responsibility of controlling the gaining and sharing knowledge during the junior and senior phases throughout the optimization process. Moreover, an adaptive scheme is presented to control the knowledge rate in order to simulate the gaining and sharing experience throughout the human being life span for a specific population by taking into consideration the diverse nature of any population. a non-linear reduction for the population size is used to improve the GSK algorithm’s performance. Finally, a larger initial value of the initial population size (NP) for all dimensions are applied. The presented algorithm (APGSK) is tested on a very recent benchmark testbed on bound constrained numerical optimization that composed of distinct challenging optimization problems with different dimensions. The presented algorithm (APGSK) showed a superior performance comparing with the original GSK and with other four state-of-the-arts algorithms and the winners of CEC2020 competition.

A 10-digit chipless RFID (CRFID) based numeric keypad that works on the principle of touch event sensing is presented. The tag is designed to operate using passive frequency domain technique. The proposed tag has ten circular resonator rings for each of the keypad’s corresponding decimal digit. Each circular ring resonates at a particular frequency within 400 MHz band from 2.7-7.6 GHz to uniquely identify a digit. Identification is performed by decoding RCS response of the whole keypad tag. A customized human finger using standard tissue characteristics is modelled to verify the keypad’s working mechanism. When the finger is placed on any of the nine resonator its corresponding resonance disappears from the allocated band. The proposed CRFID keypad Sensor tag is built upon a Rogers substrate of 85×95×0.8 mm ³ volume. The backside of the proposed tag is fully copper filled for an easy interface with a variety of backgrounds including metallic walls. The design exactly mimics the shape of ordinary keypad. The finger model used in the simulations realizes the practical scenario. This CRFID keypad is deemed an intriguing solution for future automated cities and will help in the conceptualization of IoT systems.

We propose a quad-stub microwave differential sensor which exhibits a single electromagnetic transparency window and Fano resonance when operated under symmetrical resonant conditions. When the resonance balance is slightly disturbed by introducing small dielectric variations on one of the stub pairs, a second transparency window appears in the stop-band whose amplitude corresponds to the extent of the differential imbalance. The narrow sensing window falls within the frequency range of 2.25 – 2.5 GHz. The differential sensing can be exploited in highly sensitive chemical sensing problems such as to differentiate quality of biofuel samples. Experimentally, the concept has been demonstrated by detecting ethanol concentration in aqueous solutions with varying molality. The high sensitivity is shown by resolving a 2.5 % v/v ethanol-aqueous concentration. The compact design facilitates measurements for sample volumes as small as approximately 20 $\mu$ L. A good agreement is demonstrated between simulation and experimental results. Overall, the proposed sensor possesses a simple and compact geometry that can also be scaled to operate at optical frequencies. Furthermore, a metal slot waveguide structure is also designed to demonstrate the differential sensing mechanism adaptability to THz frequencies. The ease of operation and its capability to detect minor differences samples make it a competent candidate for high-sensitivity sensing applications in biofuel detection and biomedical sensing.