Synthesis Of Linear Array Antennas Research Articles

Optimal Pattern Synthesis of Linear Array Antennas Using the Nonlinear Chaotic Grey Wolf Algorithm

The grey wolf optimization (GWO) algorithm is a new nature-inspired meta-heuristic algorithm inspired by the social hierarchy and hunting behavior of grey wolves. In this paper, the GWO algorithm is improved to overcome previous shortcomings of being easily trapped in local optima and having a low convergence rate. The proposed enhancement of the GWO algorithm utilizes logistic-tent double mapping to generate initialized populations, which enhances its global search capability and convergence rate. This improvement is called the nonlinear chaotic grey wolf optimization (NCGWO) algorithm. The performance of the NCGWO algorithm was evaluated with four representative benchmark functions. Then, the NCGWO algorithm was applied to perform an optimal pattern synthesis of linear array antennas (LAAs) using two distinct approaches: optimizing the amplitudes of the antenna currents while preserving uniform spacing and optimizing the positions of the antennas while assuming uniform excitation. To validate the effectiveness of the proposed approach, the results obtained by the NCGWO algorithm were compared with those obtained by other intelligent algorithms. Additionally, the NCGWO algorithm was applied to a more complex planar antenna array to further validate its performance. Our results demonstrate that the NCGWO algorithm exhibits superior performance regarding electromagnetic optimization problems compared to widely recognized algorithms.

Synthesis of Linear Array Antenna with Wide Range of beamwidths using DE Algorithm

In many communication systems like Radar and point to point micro links there is a need of both narrow and wide beams. In target applications Antennas with narrow beam width is required on the other hand, for spy application antennas with wider beam width are required. Generally, for linear array antennas the beams width will be narrowed by increase in the number of elements in the array. To obtain narrow beams without a significant increase in the number of radiation elements in the array, the optimization algorithms are used. By the use of optimization algorithms the wider beams can also be employed in reverse procedure of choosing the highest output for a given set of random inputs. These wide ranges of beam width are obtained without any increase in the side lobe levels that is, by maintaining the side lobe level below -20db. Random stochastic methods like Particle swarm optimization (PSO), Genetic algorithm (GA), Biogeography-based optimization (BBO) and Differential Evolution Algorithm (DE) are used and the results are compared.

Synthesis of Linear Array Antenna for Required Range of Beam Width Using Optimization Algorithms

In an application like radar there is a need for a wide range of Beam widths depending on whether the radar is operating in search mode or tracking mode. Wide range of beam widths can be achieved by using optimization algorithms like Biogeography-based optimization (BBO) and Differential Evolution Algorithm (DE). The desired beam width should be achieved without any significant increase in the side lobe level (SLL). This can be done by optimizing both SLL and FNBW simultaneously. Synthesis of linear array antenna for a fixed range of beam width is obtained by using the proposed methodology. The results for simultaneous optimization of FNBW and SLL using BBO and DE algorithms are compared.

PHYSICAL THEORY FOR PARTICLE SWARM OPTIMIZATION

We propose an inter-disciplinary approach to particle swarm optimization (PSO) by establishing a molecular dynamics (MD) formulation of the algorithm, leading to a physical theory for the swarm environment. The physical theory provides new insights on the operational mechanism of the PSO method. In particular, a thermodynamic analysis, which is based on the MD formulation, is introduced to provide deeper understanding of the convergence behavior of the basic classical PSO algorithm. The thermodynamic theory is used to propose a new acceleration technique for the PSO. This technique is applied to the problem of synthesis of linear array antennas and very good improvement in the convergence performance is observed. A macroscopic study of the PSO is conducted by formulating a diffusion model for the swarm environment. The Einstein’s diffusion equation is solved for the corresponding probability density function (pdf) of the particles trajectory. The diffusion model for the classical PSO is used, in conjunction with Schrodinger’s equation for the quantum PSO, to propose a generalized version of the PSO algorithm based on the theory of Markov chains. This unifies the two versions of the PSO, classical and quantum, by eliminating the velocity and introducing position-only update equations based on the probability law of the method.