Synthesis of linear sparse arrays based on dynamic parameters differential evolution algorithm

Abstract

Synthesis of antenna arrays is a momentous technology possessing many advantages and broad prospects. Dynamic parameters differential evolution (DPDE) with two modifications is proposed for the synthesis of linear sparse arrays (LSA) which aims at reducing peak side‐lobe level. One modification is that the scaling factor decreases monotonically over iterations, making exploration in the later period more efficient and accuracy. The other modification is that the crossover probability changes dynamically during iterative process through a novel self‐adaptive strategy. Experiments of checking on benchmark functions and simulations of synthesising LSA validate that DPDE performs better than reference methods convincingly.

Synthesis of Sparse Arrays Based On CIGA (Convex Improved Genetic Algorithm)

“…4 (b) depicts the PSLL after that the proposed method runs 50 times. The best PSLL is 29.02 -dB and the mean PSLL is 28.55 -dB, as Table Ⅱ shows, which is about 5dB better than the PSLL obtained by these algorithms mentioned in [12]. Table Ⅳ, Table Ⅴ.…”

“…The proposed algorithm has the advantages of the IGA and the convex optimization method. The PSLL obtained by the CIGA can get 20% reduction of that of the swarm intelligent optimization algorithms [12]- [14], [21] in sparse linear arrays. In sparse planar arrays, the PSLL obtained by the CIGA can get 7% reduction of these methods mentioned in [13].…”

“…In order to illustrate the superiority of the proposed algorithm in this paper, the CIGA is compared with Dynamic parameters differential evolution algorithm (DPDE) [12], Two…”

“…In the past few years, many synthesis methods of antenna arrays have been developed. The methods of antenna arrays synthesis based on stochastic optimization algorithm are proved effectively, such as genetic algorithm (GA) [6,7], differential evolution (DE) [8], particle swarm optimization (PSO) [9], invasive weed optimization [10] and cuckoo search algorithm [11], dynamic parameters differential evolution algorithm (DPDE) [12], improved chicken swarm optimization [13]. Such methods are effective for optimizing amplitude and phase of the excitation coefficients of the equally spaced arrays or optimizing the element positions with the uniform excitation coefficients, but it cannot achieve the truly minimum PSLL when both the excitation coefficients and the element positions are…”

Design of low sidelobe series microstrip array antenna with non‐uniform spacing and excitation amplitude

“…Based on [14 ], this Letter takes array element spacing and excitation amplitude as optimisation variables at the same time, and constructs a two‐variable DEA.…”