  During the last decades, the global prevalence of dengue progressed
dramatically. It is a disease that is now endemic in more than one hundred
countries of Africa, America, Asia, and the Western Pacific. In this paper, we
present a mathematical model for the dengue disease transmission described by a
system of ordinary differential equations and propose a multiobjective approach
to find the most effective ways of controlling the disease. We use evolutionary
multiobjective optimization (EMO) algorithms to solve the resulting
optimization problem, providing the performance comparison of different
algorithms. The obtained results show that the multiobjective approach is an
effective tool to solve the problem, giving higher quality and wider range of
solutions compared to the traditional technique. The obtained trade-offs
provide a valuable information about the dynamics of infection transmissions
and can be used as an input in the process of planning the intervention
measures by the health authorities. Additionally, a suggested hybrid EMO
algorithm produces highly superior performance compared to five other
state-of-the-art EMO algorithms, being indispensable to efficiently optimize
the proposed model.
