Pattern Synthesis of Uniform and Sparse Linear Antenna Array Using Mayfly Algorithm

Abstract

Pattern synthesis is a significant research focus in smart antennas due to its extensive use in several radar and communication systems. To improve the optimization performance of pattern synthesis of uniform and sparse linear antenna array, this paper presents an optimization method for solving the antenna array synthesis problem using the Mayfly Algorithm (MA). MA is a new heuristic algorithm inspired by the flight behavior as well as the mating process of mayflies, it has a unique velocity update system with great convergence. In this work, the MA was applied to linear antenna arrays (LAA) for optimal pattern synthesis in the following ways: by optimizing the antenna current amplitudes while maintaining uniform spacing and by optimizing the antenna positions while assuming a uniform excitation. Constraints of inter-element spacing and aperture length are imposed in the synthesis of sparse LAA. Sidelobe level (SLL) suppression with the placement of nulls in the specified directions is also implemented. The results gotten from this novel algorithm are validated by benchmarking with results obtained using other intelligent algorithms. In the synthesis of uniform 20-element LAA with nulls, MA achieved an SLL of -31.27 dB and the deepest null of -101.60 dB. Also, for sparse 20-element LAA, an SLL of -18.85 dB was attained alongside the deepest null of -87.37 dB. MA obtained an SLL of -35.73 dB and -23.68 dB for the synthesis of uniform and sparse 32-element LAA respectively. Finally, electromagnetism simulations are conducted using ANSYS Electromagnetics (HFSS) software, to evaluate the performance of MA for the beam pattern optimizations, taking into consideration the mutual coupling effects. The results prove that optimization of LAA using MA provides considerable enhancements in peak SLL suppression, null control, and convergence rate with respect to the uniform array and the synthesis obtained from other existing optimization techniques.

Multi-Objective Optimization with Mayfly Algorithm for Periodic Charging in Wireless Rechargeable Sensor Networks

“…MA is a new algorithm that has not been used before for energy harvesting in WRSN and is a type of optimization algorithm that can accomplish this objective. It is a recently created method that incorporates the key advantages of PSO, GA, and FA, where its superiority in terms of convergence rate and speed was demonstrated in [37,38] and shown to be good. Because it has yet to be implemented in a variety of engineering optimization disciplines, we propose using it to solve energy optimization problems.…”

Advanced Marine Predator Algorithm for Circular Antenna Array Pattern Synthesis

“…Many research works have been dedicated to developing techniques for the pattern synthesis of antenna arrays with a narrow main lobe and reduced sidelobe level [ 2 , 6 ]. Pattern synthesis of different types of antenna such as linear array, circular array, hexagonal array, concentric circular array, time modulated arrays, and many more using some heuristic algorithms has been carried out by several researchers [ 7 , 8 , 9 , 10 , 11 ]. To solve the antenna array optimization, the choice of the optimization algorithm is essential, as is the objective function capturing the designated relevance of the antenna array.…”

“…Mutual coupling is likewise a relevant factor to consider in the design of the antenna array, for it has a great effect on the field performance of the antennas. Thus, recent research work carefully put into consideration the mutual coupling effect during the antenna array synthesis [ 7 , 9 , 12 ].…”

Least Perturbation Based Method of Multi-Objective Null Placement in Linear Antenna Array Using Evolutionary Algorithms

“…Traditionally, suppression of signal originating from unwanted sources is carried out by masking the interference signal in a particular direction. Accordingly, null positioning methods in antenna array systems are based on an analytical approach [1][2][3][4][5], [7], [10], [15], [18] and by using evolutionary algorithms [6], [8], [9], [11][12][13][14], [16], [17], [19][20][21][22][23][24][25][26][27][28] have been implemented by researchers, for achieving single, multiple and wide null placement in beam steered linear array. In those approaches, phase [6], [8] [9], [12], element spacing [7], [13], [14], [24], [27] and real valued excitation [10], [15], [16], [18][19][20], [28] have been controlled individually or in combination [11], …”

“…Taguchi Method [16], Spider Monkey optimization [20], Flower Pollination Algorithm [21], [23], Cross Entropy Based Optimization [24], Strawberry Optimization Algorithm [25] and Mayfly Algorithm [28] have been used for null placement. All these algorithms control the excitation amplitude, inter-element spacing, phase individually or in combination of aforementioned parameters for achieving null placement under multi objective scenario.…”