Abstract
The aim of the research is to propose a new optimization method for the multiconstrained optimization of sparse linear arrays (including the constraints of the number of elements, the aperture of arrays, and the minimum distance between adjacent elements). The new method is a modified wolf pack optimization algorithm based on the quantum theory. In the new method, wolves are coded by Bloch spherical coordinates of quantum bits, updated by quantum revolving gates, and selectively adaptively mutated when performing poorly. Because of the three-coordinate characteristics of the sphere, the number of global optimum solutions is greatly expanded and ultimately can be searched with a higher probability. Selective mutation enhances the robustness of the algorithm and improves the search speed. Furthermore, because the size of each dimension of Bloch spherical coordinates is always [−1, 1], the variables transformed by solution space must satisfy the constraints of the aperture of arrays and the minimum distance between adjacent elements, which effectively avoids infallible solutions in the process of updating and mutating the position of the wolf group, reduces the judgment steps, and improves the efficiency of optimization. The validity and robustness of the proposed method are verified by the simulation of two typical examples, and the optimization efficiency of the proposed method is higher than the existing methods.
Highlights
Since the 1960s, the sparse array has been widely studied for its high target resolution and low cost
We introduced quantum Bloch spherical coordinates into the Wolf pack algorithm (WPA) and proposed a modified quantum wolf pack algorithm (MQWPA) based on Bloch spherical coordinates. en, the new algorithm is applied to the multiconstraint synthesis of sparse linear arrays
MQWPA for Antenna Pattern Synthesis. rough the extremum optimization experiments of various typical test functions, we find that the MQWPA is very suitable for solving complex nonlinear optimization problems. erefore, this paper applies the MQWPA to the multiconstraint synthesis of the symmetric one-dimensional sparse linear array, hoping to get good results
Summary
Since the 1960s, the sparse array has been widely studied for its high target resolution and low cost. Compared with uniform array synthesis, the optimization of nonuniform array placement has always been a difficult problem To solve this problem, many synthesis methods have been proposed, such as dynamic programming [11], fractional Legendre transform [12], simulated annealing [13], particle swarm optimization [14,15,16], and genetic algorithm [17,18,19,20,21]. When the genetic algorithm is used to optimize the element position of the sparse array, sometimes it is difficult to obtain satisfactory solution in limited time. Aiming at the first problem, the improved genetic algorithm is used in the literature [17, 18, 20] to optimize the sparse array element spacing and amplitude weighting. Compared with the existing literature methods, this paper presents a new scheme for the multiconstraint synthesis of one-dimensional sparse linear array, which has higher optimization efficiency
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