APPLICATION OF CHAOTIC PARTICLE SWARM OPTIMIZATION ALGORITHM TO PATTERN SYNTHESIS OF ANTENNA ARRAYS

To deal with pattern synthesis of antenna arrays, a chaotic particle swarm optimization (CPSO) is presented to avoid the premature convergence. By fusing with the ergodic and stochastic chaos, the novel algorithm explores the global optimum with the comprehensive learning strategy. The chaotic searching region can be adjusted adaptively. To evaluate the performance of CPSO, several representative benchmark functions are minimized using various optimization algorithms. Numerical results demonstrate that the proposed approach improves the performance of the algorithm significantly, in terms of both the convergence speed and exploration ability. Moreover, CPSO was applied to array synthesis examples, including the equally spaced linear array, unequally spaced linear array and conformal array, compared with other optimization methods. Experimental results show its high performance in the pattern synthesis with low side lobe, multi-nulls and shaped beam.