A Novel Cooperative Immune-Based Algorithm for Dynamic Optimization Problems

environments. To efficiently exploit the search space, the algorithm divides each dimension into some regions, and each dimension is evaluated separately. Also, inspired by the immune memory concept, a memory-based approach is proposed to store and retrieve the most important information when a change occurs. Furthermore, to increase the convergence speed, a new clonal selection method is proposed. Lastly, the efficiency of the algorithm is compared to that of other immune-based algorithms on the MPB. The experimental results confirm that the proposed method is competitive to optimize dynamic problems.

Keywords:

Clonal selection algorithm , Negative selection mechanism , Artificial immune system , Dynamic optimization