An Efficient and Universal Static and Dynamic Convex Optimization for Array Synthesis

Abstract

In this letter, a novel static-dynamic convex optimization algorithm is proposed for array synthesis, which can be divided into static and dynamic successive optimization stages. At the first stage, the synthetic direction graph is constructed as a second-order cone programming problem so that an initial excitation is quickly obtained. Next, by serving the results obtained previously as the initial excitation, an iterative operation is implemented to obtain a high-precise solution. Moreover, in order to effectively ensure the continuity of the solution process and approach the feasible solution, shrinkage factor and crossover operator are introduced. Four tough examples of synthesizing different kinds of patterns are conducted to validate the feasibility and effectiveness of the proposed method. Results show that compared with the existing representative methods, the proposed algorithm is of high computational efficiency and universality.