In this paper, a new hybrid optimization approach for the pattern synthesis of linear antenna array is proposed. Hybrid approach (TAGAPSO) contains Taguchi Algorithm(TA), Genetic Algorithm(GA) and Particle Swarm Optimization (PSO). The aim of the optimization is to obtain desired side lobe level and half power beam width of the linear antenna array. Optimization results obtained by TA, GA, PSO and TAGAPSO are compared. The results show that TAGAPSO is much better than TA, GA and PSO. Optimization goals satisfied via TAGAPSO are much better than the other methods. Results of TAGAPSO are also compared with the results obtained by High Frequency Structural Simulator (HFSS) in order to verify the results.

In this work, particle swarm optimization (PSO) based approach to the synthesis of a cylindrical-rectangular ring microstrip conformal antenna using support vector regression (SVR) models is presented. Resonant frequency of the antenna is obtained by PSO of trained SVR models. Radial basis function (RBF) and wavelet kernel functions are used in SVR models. Simulation examples are given and the results are compared.

The design process of antenna structures that meet up-to-date requirements takes a long time and brings a high computational load. In this paper, an approach based on Soft Computing (SC) techniques was used to shorten the design time and to achieve an antenna structure that yields performance characteristics as close as possible to the desired values. In order to obtain a microstrip patch antenna with the targeted characteristics and the best accuracy in a faster way, a Support Vector Machine (SVM)-based regression model was employed. A triple-band microstrip antenna with desired resonance frequencies and gain values was designed by using the Support Vector Regression (SVR) model by introducing multiple slots and arc-truncation to the patch antenna. Simulation results of the High-Frequency Structural Simulator (HFSS) and measurements of implementation of the designed antenna are given. Performance characteristics of the obtained antenna are also compared with those given in the literature, which have triple-band properties. In addition, the antenna was redesigned using the optimization tool in HFSS for comparison. The accuracy of the results and required time for design were compared for both the SVR model approach and the HFSS optimization tool.

A genetic-based synthesis approach to the various types of rectangular microstrip antenna elements with specified mutual coupling coefficient is proposed. Continuous parameter genetic algorithm (CPGA) is used for the synthesis process. Examples for the synthesis of a rectangular microstrip antenna (RMSA), a rectangular ring microstrip antenna (RRMSA) with smaller slot dimension and a RMSA with a pair of offset slots (RMSAPOS) are given.

In this paper, the design of a triple band rectangular microstrip antenna with triple slots for WLAN and 5G applications has been presented. Support Vector Regression (SVR) model has been used in the design procedure. Simulation results of High Frequency Structural Simulator (HFSS) are given and compared with a traditional rectangular antenna without slots as well as with the results of some triple band antennas given in the literature.

In this paper, Hybrid Taguchi Genetic Algorithm (HTGA) is used to minimize the side lobe level and to keep half power beam width in certain values for cylindrical conformal antenna array. The HTGA is based on Genetic Algorithm (GA) and Taguchi Algorithm (TA). Results obtained by GA, TA and HTGA are compared. The best results are also compared with the results obtained by HFSS (High Frequency Structural Simulator).