Conformal Antenna Array Pattern Synthesis Using Genetic Learning Particle Swarm Optimization Algorithm

Investigations on conformal phased array pattern synthesis using genetic learning particle swarm optimization algorithm (GL-PSO) are presented in this paper. The hybrid algorithm is composed of two cascading layers, the first for exemplar generation and the second for particle update. Genetic operators are used to generate exemplars from which particles learn and, in turn, historical search information of particles provides guidance to the evolution of the exemplars. The hybrid algorithm is applied to synthesize radiation pattern of a 4×2 cylindrical conformal microstrip antenna . The excitation weights of the conformal array elements are optimized to obtain specific radiation pattern including scanning angle, reduced SLL, limited beamwidth. The influence of mutual coupling and conformal platform are fully considered in the optimization process. Experimental results have verified that the GL-PSO has fast convergence speed and high convergence accuracy when applied to antenna array pattern synthesis. Introduction Recently, conformal antenna array has attracted a lot of attention in many applications due to their capacity for beam steering, beam shaping, high gain, visual unobtrusiveness and non-interference with the aerodynamic performance and antenna performance. Various methods including traditional mathematical techniques and optimization algorithms have been proposed to solve complex antenna pattern synthesis problems. But low side-lobe array pattern synthesis techniques developed for planar and linear arrays do not work well with conformal arrays if the array is conformal to a curved surface. This is due to that the elements of conformal array generally direct their radiation beams toward different directions, and not all elements contribute equally to the desired pattern. Therefore, it is desirable to develop an algorithm which is robust and has excellent global optimization performance and fast convergence speed for conformal antenna array pattern synthesis. As one of the most important global optimization algorithms, the evolutionary optimization algorithm derives from biologic behavior and is especially suitable for solving strongly nonlinear multi-parameter multi-goal problems of conformal array synthesis. Various evolutionary algorithms (EAs) have become more and more popular in this community as they are applicable to both regular and irregular arrays, with or without constraints. Genetic algorithm (GA) and particle swarm optimization (PSO) are two popular kinds of evolutionary optimization algorithms and they are getting much attention due to their efficiency and simplicity [1]. GA uses biologically inspired techniques, such as natural selection, crossover and mutation. The PSO algorithm has only one velocity operator, which renews the current positions of the particles, changes the cooperation or social knowledge of the swarms, and makes the particles move toward a region containing the global or near-optimal optimal solution. However, the disadvantage of these two algorithm when applied to antenna array pattern synthesis is the premature convergence. Hybridisation has been a popular strategy for improving performance of a single algorithm [2-4]. To further improve the performance of PSO, a genetic learning scheme that applies GA for exemplar construction [5] is adopted in this