Optimization of Complex Hierarchical Problems With Swarm Intelligence and Network Science

Swarm Intelligence and Network Science Swarm Intelligence and Network Science emerge as a promising approach for modeling and optimizing complex systems, as both share characteristics with distributed systems, exhibit emergent behavior, involve interactions between components, and take nto account the dynamic nature of systems. It represents an important strategy for improving the resolution of optimization in various real, natural, and social systems, aiming for better efficiency, interpretability and robustness. This work proposes a hybridization to optimize hierarchical complex systems, considering a landscape with many followers, multi objectives and dynamic processes. The expected results are a more efficient modeling of the problems and the practical application of optimization techniques after the prediction process. The expected contribution includes progress of in Swarm Intelligence with Network Science techniques to real problems and a reduction existents gaps.