Synthesis of Phase-Only Reconfigurable Linear Arrays Using Multiobjective Invasive Weed Optimization Based on Decomposition

Abstract

Synthesis of phase-only reconfigurable array aims at finding a common amplitude distribution and different phase distributions for the array to form different patterns. In this paper, the synthesis problem is formulated as a multiobjective optimization problem and solved by a new proposed algorithm MOEA/D-IWO. First, novel strategies are introduced in invasive weed optimization (IWO) to make original IWO fit for solving multiobjective optimization problems; then, the modified IWO is integrated into the framework of the recently well proved competitive multiobjective optimization algorithm MOEA/D to form a new competitive MOEA/D-IWO algorithm. At last, two sets of experiments are carried out to illustrate the effectiveness of MOEA/D-IWO. In addition, MOEA/D-IWO is compared with MOEA/D-DE, a new version of MOEA/D. The comparing results show the superiority of MOEA/D-IWO and indicate its potential for solving the antenna array synthesis problems.

MOEA/D with Adaptive IWO for Synthesizing Phase-Only Reconfigurable Linear Arrays

“…It can be seen from the two figures that MOEA/D-AIWO performs the best. From the Pareto set, we choose the best compromise solution [20] obtained by each algorithm and present them in Figs. (3), (5) and Tables 2, 3.…”

“…To demonstrate the performance of the proposed MOEA/D-AIWO in the design of phase-only pattern reconfigurable linear arrays, we carry out two sets of experiments, and compare the experimental results with those obtained by MOEA/D-DE [12] and MOEA/D-IWO [20]. In the optimizing process, excitation amplitudes range from 0 to 1, and phases are restricted from -180 to 180 degrees.…”

“…In experiments, H is set to 200 and 23 for twoobjective and three-objective instance respectively, therefore, the population size P is 201 for two-pattern and 300 for three-pattern reconfigurable arrays design. About the values setting of the other parameters in Table 1, we refer related references [12,20]. Detailed descriptions can be found in [12,20], we do not redescribe them here.…”

“…About the values setting of the other parameters in Table 1, we refer related references [12,20]. Detailed descriptions can be found in [12,20], we do not redescribe them here. To be fair in experimental comparisons, we set the maximum function evaluation 500000, and all these three algorithms (MOEA/D-AIWO, MOEA/D-IWO, and MOEA/D-DE) stop after reaching the maximum function evaluation.…”

“…With strong robustness and adaptability, IWO converges to the optimal solution effectively. It has been proved that IWO can successfully solve many single-objective optimization problems [14][15][16][17][18] and some MOPs [19,20].…”