Optimizaton using clonal selection algorithm and immune memory based on self-organizing map

In this work an approach of integration of clonal selection algorithm and immune memory based on self-organizing map(SOM) is presented to solve optimization problem. Immune memory lays the foundation for a rapid and massive secondary response of immune system. Management of immune memory is important for improving performance and quality of optimum search using immune algorithm. The adaptive functionality of SOM is applied for emulation of the dynamic behavior of immune memory. From results obtained using proposed approach SOM-based management of immune memory can keep balance between exploration and exploitation for good solution quality and search performance. Besides SOM can improve the clonal selection algorithm in performance for multi-modal optimization search.