A Modified Immune Optimization Algorithm

This paper proposes a modified immune optimization algorithm for the multi-modal problems. It is inspired by the clonal selection and antibody diversity maintaining principles in the immunology. Compared with the existing immune optimization algorithms, our new algorithm defines the concentration of the antibody to stand for the solution diversity, and the mutation rate is dynamically adjusted based on the antibody concentration and fitness. Moreover, this algorithm takes advantage of the clusters of antibodies. For each cluster, the elitist ones are always retained as the memory set. Therefore, an improved trade-off between the exploration and exploitation in the solution space can be achieved. A few multi-modal and high-dimension benchmark functions are utilized here to examine the efficiency of our optimization method. Performance comparisons are also made with the CLONALG and opt-aiNET. Simulation results demonstrate that this modified optimization algorithm can effectively obtain the optimal solutions, and still maintain the solution diversity, which is crucial for dealing with challenging real-world optimization problems