Immune algorithm in pattern synthesis of linear array

Immune algorithm (IA) is presented for pattern synthesis of linear array in this paper. IA is an efficient approach to escape from local minima and maxima which is suited for multi-objective optimization. Logistic mapping is used for enhancing the diversity of the initial antibody colony with the advantages of randomicity, ergodicity and initial sensitivity. Further more, gauss function is applied in the mutation process to improve the searching efficiency. For the examples of applying IA in linear array synthesis, sidelobe level (SLL) reduction and null steering are optimized, and well results show the fine validity of the proposed IA.