Abstract
Multimodal optimization algorithms inspired by the immune system are generally characterized by a dynamic control of the population size and by diversity maintenance along the search. One of the most popular proposals is denoted opt-aiNet (artificial immune network for optimization) and is extended here to deal with time-varying fitness functions. Additional procedures are designed to improve the overall performance and the robustness of the immune-inspired approach, giving rise to a version for dynamic optimization, denoted dopt-aiNet. Firstly, challenging benchmark problems in static multimodal optimization are considered to validate the new proposal. No parameter adjustment is necessary to adapt the algorithm according to the peculiarities of each problem. In the sequence, dynamic environments are considered, and usual evaluation indices are adopted to assess the performance of dopt-aiNet and compare with alternative solution procedures available in the literature.