A novel IGA-edsPSO Hybrid algorithm for the synthesis of sparse arrays

Abstract

Based on the improvements of both Genetic Algorithm and Particle Swarm Optimization, a novel IGA-edsPSO (Improved Genetic Algorithm-extremum disturbed simple Particle Swarm Optimization) Hybrid algorithm is proposed in this paper. An improved performance of GA is achieved by reducing the array space. By discarding the particle velocity vector in the PSO evolutionary equation, the sPSO (simple PSO) can avoid the problem of slow later convergence velocity and low precision caused by determining the maximal velocity vector factitiously. And the edsPSO can overstep local extremum point more effectively with the help of the extremum disturbed factor. The proposed IGA-edsPSO Hybrid algorithm is used in the design of the sparse arrays with minimum element spacingconstraint. Given the array aperture and the number of the array elements, the suppression of the peak sidelobe level (PSLL) with a certain half power beam-width (HPBW) restriction is implemented with a high efficiency by optimizing the HPBW and PSLL synchronously. The simulation results show that faster convergence velocity (which means less computation time) and lower sidelobe level are obtained using IGA-edsPSO compared to IGA, standard PSO, GA-PSO and GA-sPSO.