Linear sparse arrays designed by dynamic constrained multi-objective evolutionary algorithm - Wei Dong; Sanyou Zeng; Yong Wu; Dayue Guo; Lunan Qiao; Zhiqun Liu (2014 IEEE Congress on Evolutionary Computation (CEC), 2014)
The design of linear sparse array is a constrained multi-objective optimization problem(CMOP). There are three objectives: minimization of peak sidelobe level(PSLL), half-power beam width(HPBW) and spatial aperture. The amplitude coefficients of elements and sensor positions of the array are decision variables. Dynamic constrained multi-objective evolutionary algorithm(DCMOEA) is used to design linear sparse arrays in this paper. It makes a difference that the output is a set of Pareto solutions (antenna arrays), not just only one solution. The users can choose an array from the set to meet their preferences for low PSLL, small HPBW, small spatial aperture or a trade-off among them. Experimental results showed that the DCMOEA performs better than peer state-of-art algorithms referred in this paper, especially on the arrays' spatial aperture optimization.