| Intelligent optimization algorithm gets rid of the bondage of classicalmathematical programming method and build a visualized calculation model tosolve the optimization problems according to the nature. In the decades ofresearch and development, it has become the one of the powerful tool to solvethe problems in many field. In the early1990s, intelligent optimizationalgorithm involved in electromagnetic filed. Since then, intelligent optimizationalgorithm begin to applied to design and optimization of antenna, and Shows theexcellent advantages in performance. Intelligent algorithm has become animportant method in design of antenna and other electromagnetic component.Ant colony optimization (ACO) algorithm which imitates the forgaing activitiesof ants is a kind of heuristic intelligent algorithm. The high search speed andefficiency make it widely used in communication engineering, automaticcontrol, management and other fileds. In this paper Genetic-Ant ColonyOptimization algorithm is studied and applied to design of microstrip antennaand photonic crystals. As an important part of wireless communication system, the performanceof antenna determine the performance of communication system. Thecharacteristics of Microstrip antenna which is low profile and small size cater tothe needs of the development trend of modern communication system which isminiaturization and micromation. Microstrip antenna become a global hotresearch field. For some inherent defects, the researchers spare no effortto improve the performance of microstrip antenna. The emergence of photoniccrystal and its successful application in microwave provides a new way to theimprovement of microstrip antenna. By loading photonic crystals on themicrostrip antenna, the surface wave in the substrate can be weakened, whichgreatly enhanced the radiation efficiency of antenna.In this paper Ant Colony Optimization (GACO) algorithm is proposed,named Genetic-Ant Colony Optimization algorithm. Through being embeddedin genetic operation, the diversity of Feasible solution of the algorithm isincreased, so the global search ability is enhanced. GACO is applied to designof microstrip antenna. The main content of the paper is as follows:(1)Genetic-Ant Colony Optimization algorithm. In view of theshortcoming of traditional Ant Colony algorithm which is easy to fall in to localoptimal, the improved algorithm GACO is proposed. Through six test functionsto prove the superiority of GACO. (2)Design of microstrip antenna. The GACO is combined with HFSSsoftware to used in design of U-slot rectangular micrstrip antenna andbraodband microstrip antenna. All the antennas get a better performance.(3)Design of crystal microstrip antenna. Use the GACO algorithm and“pixel” method to design a uncoplanar compact photonic crystal unit, andapplied the unit to microstrip antenna. The performance of the antenna isobviously improved.(4)Design of microstrip antenna array. Using GACO algorithm to designuncoplanar compact photonic crystal structure between the array unit. Byloading the photonic crystal, the mutual coupling effect is obviously weakened. |
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