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Based on quantum theory, this paper proposed an improved wolf pack optimization algorithm for the comprehensive optimization of sparse linear arrays with multiple constraints. The new algorithm greatly improved the global optimization ability through the special coding of quantum bits, stagnation detection and selective mutation. It gave the flow chart of the improved wolf swarm algorithm. Given the array aperture and the number of elements, the algorithm could simulate the peak side-lobe level( PSLL) of sparse linear arrays with arbitrary minimum array spacing constraints. By solving the space transformation, it effectively avoided the infeasible solution in the process of updating wolf group position, reduced the judgment steps, and improved the optimization efficiency. The simulation results show that the method is efficient and stable, and has good popularization value. [ABSTRACT FROM AUTHOR]

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