An Improved Gravity Search Algorithm and Its Application in Planar Array Synthesis

An improved gravity search algorithm, adaptive gravity search algorithm (AGSA), is proposed to solve the problem that the gravity neutralization caused by the cumulative effect of particle inertia mass at the end of iteration, which will affect the optimization performance. An adaptive decay factor is designed, which can produce different gravitation values at different iteration stages of the algorithm and accelerate the mining ability of the algorithm at the later iteration stage. In order to enhance the memory ability of the algorithm, the influence of elite particles is added to the realization of the speed to expand the exploration ability. The improved algorithm is used to optimize uniform concentric ring array, the main lobe width optimized by the AGSA is 6.7°narrower and the side lobe level is 5.1 dB and 1.8 dB lower than the algorithm in the literature. It is clear that the pattern obtained by AGSA meets the desired pattern very well. Moreover, when the number of iterations is 2 000, the fitness value of the improved algorithm is increased by 30%. It can be seen that AGSA outperforms the algorithm in the literature in evolutionary speed and accuracy. Sparse concentric ring array also has the same optimization results. The effectiveness of the proposed improved algorithm in solving the array pattern synthesis is proved.