ABSTRACT The novel implementation of three evolutionary optimisation algorithms, namely quantum particle swarm optimisation (QPSO), teaching-learning based optimisation (TLBO) and symbiotic organism search (SOS) for thinning of multiple concentric circular array antenna with isotropic array elements for the simultaneous optimisation of the sidelobe levels (SLLs) as well as peak directivity is presented in this work. The performance studies as cases 1 and 2 are made with two control parameters: interring radii and number of switched ‘on–off’ elements in each ring. The comparative study of the three algorithms has been carried out using common parameters. Finally, experimental results show that case 2 outperforms case 1 with regard to SLL and directivity. Apart from this, the results of SOS have been shown to be better than the other two state of art meta-heuristic optimisation after comparing their effectiveness based on SLL, peak directivity, mean value, and standard deviation along with best cost. For statistical validation of both cases, t-test has been done for testing the stability of SOS over QPSO and TLBO.

中文翻译：

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带约束减薄多同心圆阵列天线优化合成的进化算法

摘要 三种进化优化算法的新实现，即量子粒子群优化 (QPSO)、基于教学的优化 (TLBO) 和共生生物搜索 (SOS)，用于减薄具有各向同性阵列单元的多个同心圆阵列天线，用于同时优化在这项工作中介绍了旁瓣电平 (SLL) 以及峰值方向性。案例 1 和案例 2 的性能研究使用两个控制参数进行：环间半径和每个环中“开-关”元件的数量。使用共同参数对三种算法进行了比较研究。最后，实验结果表明，案例 2 在 SLL 和方向性方面优于案例 1。除此之外，在比较基于 SLL、峰值方向性、平均值和标准偏差以及最佳成本的有效性后，SOS 的结果已被证明优于其他两种最先进的元启发式优化。对于这两种情况的统计验证，已经进行了 t 检验以测试 SOS 相对于 QPSO 和 TLBO 的稳定性。