| Resumen |
In this paper, a memory module for the Multi-Objective Artificial
Bee Colony (MOABC) algorithm is proposed. The inclusion of a
memory has the objective of reducing the number of evaluations required by the MOABC algorithm. The proposed memory stores the fitness values for every food source, thus avoiding to evaluate more than once these sources. As case study, we present a damper optimal design. With the objective of carrying out an in-depth analysis of the suspension system, the full behavior of the suspension is considered. This is accomplished using a full car model that includes the kinematics, dynamics and geometry
of the suspension. However, simulation of the car model requires a lot of computing power which leads to long simulation times. The simulation times motivates the modifications made to the MOABC algorithm. As result of the modifications the number of evaluations needed by the MOABC is reduced to the half.
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