Abstract
In this paper, an advanced fruit fly algorithm (FOA) is proposed and applied in subarray phased array antenna synthesis. The proposed algorithm introduces orthogonal crossover, quantum selection and simulated annealing operations on the individuals, and then combines them by using an adaptive expansion-contraction factor. Accordingly, a linear generation mechanism of candidate solution based fruit fly algorithm (LGMS-FOA) is generated, in which individuals are selected in a highly balanced way, and the poor solutions are still accepted with a varying probability during the iteration. These mechanisms help the proposed algorithm enhance the population diversity and global searching capability but avoid falling into local optimum. Numerical classical unimodel benchmark functions are provided to test the proposed algorithm (OLFOA) in comparison with other advanced algorithms. In addition, to further validate its superiority, the proposed algorithm is applied to handle the subarray array synthesis of several tough planar and circular apertures with different array sizes and subarray shapes. Simulation results show that the proposed OLFOA can achieve better performance than other improved evolutionary algorithms.
Highlights
Subarray technology has attracted considerable interests in radar and communication systems, because it can greatly reduce the cost and complexity of the system by reducing the number of antenna splitters and phase shifters [1]
A modified fruit fly algorithm integrated with the linear generation mechanism of candidate solution (LGMS) was proposed in [16], showing that the algorithm performance was greatly improved without changing any natural concept
For benchmark functions F4 and F6, OLFOA obtained the best fitness value and showed a fast convergence speed. These results indicate that the proposed OLFOA algorithm outperforms numerous improved fly optimization algorithm (FOA) methods (IFFO, CEFOA, CMFOA, MFOA, and MSFOA) and the original FOA in global optimum and convergence capability
Summary
Subarray technology has attracted considerable interests in radar and communication systems, because it can greatly reduce the cost and complexity of the system by reducing the number of antenna splitters and phase shifters [1]. Similar to other evolutionary algorithms, FOA shows the disadvantages of falling into the local optimum and limited performance when calculating multi-dimensional problems To address these shortcomings, a modified fruit fly algorithm integrated with the linear generation mechanism of candidate solution (LGMS) was proposed in [16], showing that the algorithm performance was greatly improved without changing any natural concept. By introducing orthogonal cross [17] and quantum selection [19] operations on the individuals, the proposed OLFOA combines them with an adaptive expansion-contraction factor to improve the convergence performance in solving high-dimensional complex problems On this basis, the simulated annealing [20] strategy is introduced to further enhance the diversity of the population. Steps 2–6 are repeated until the terminal criterion is met or the maximum number of iterations is reached
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