Antenna arrays are used in many different systems, including radar, military systems, and wireless communications. The design of the antenna array has a significant impact on how well the communication system performs. The large number of pieces and the large sidelobe levels provide the biggest design hurdles for such arrays. The antenna arrays have recently been heavily thinned using optimization approaches that take advantage of evolutionary algorithms in order to lower power consumption and enhance the radiation pattern by lowering sidelobe levels. A global optimum for this kind of algorithm is not guaranteed, though, because of the stochastic nature of the resolution techniques. This work characterizes the optimal pattern synthesis of a linear array antenna using the Improved Particle Swarm Optimization (IPSO) algorithm. The main aim is to obtain a low Side Lobe Level (SLL) that avoids interference and a narrow beam width for acquiring high directivity to obtain the optimal solution established on the action of the swarm that adopts the fitness function. To achieve these targets, we analyze the optimization of the excitation amplitude and inter-element spacing of the array. In this article, we have presented the optimal power pattern obtained by two different types of excitation amplitude distributions for both uniformly spaced linear arrays and non-uniformly spaced linear arrays. In the first case of amplitude distribution, namely, non-uniform distribution of excitation amplitude, synthesis of the array pattern for three different values of inter-element spacing as well as optimized spacing are presented for different array sizes. In the second case, optimal thinning of a uniformly spaced array as well as a non-uniformly spaced (optimized) array has been presented. The IPSO algorithm provides a radiation pattern that is used to determine the set of antenna array parameters. The design of an antenna array using the IPSO algorithm gives significant enhancements when compared with a uniformly excited and uniformly spaced array. The flexibility as well as ease of implementation of the IPSO algorithm are evident from this analysis, showing the algorithm’s usefulness in electromagnetic optimization problems.
Background: Antennas serve a vital aspect in modern wireless communication. Designing antennas with very high directivity is very important to solve the long-distance communication problem. Though regularly excited and evenly spaced linear antenna arrays delivers good directivity but also leads to problem related to higher side lobe. For diminishing the level of side lobe, the array can be constructed either by amending the excitation amplitudes non-uniformly with all physical spaces of the antenna elements keeping consistent or vice versa.
Methods: In this work, a novel mathematical objective function has been formulated. The objective function has been solved using a recently developed evolutionary optimization technique, i. e., Binary cat swarm optimization. So for better efficiency, the cat swarm optimization technique has been modified.
Result: The results have been compared with the popular algorithms like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Cat Swarm Optimization (CSO) in terms of side lobe level (SLL), achieved fitness and execution time. The proposed algorithm achieves 0.5dB, 1.7 dB and 3dB smaller SLL as compared to CSO, PSO and GA respectively. In addition to SLL, achieved fitness using BCSO is in the range of 0.001 which is smallest among the compared algorithms.
Conclusion: it was found that the modified version namely binary cat swarm optimization algorithm outperform other well known evolutionary optimization algorithms.
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