Diversification-driven Memetic Algorithm for the Maximum Diversity Problem

The maximum diversity problem (MDP) is a classic NP-hard optimization problem with a number of applications. We propose in this work an effective hybrid evolutionary algorithm for MDP called the diversification-driven memetic algorithm by introducing a diversification mechanism into an existing memetic algorithm. Computational results on 20 representative benchmark instances show that the proposed algorithm outperforms the state-of-the-art MDP algorithms in the literature, indicating the interest of the diversification mechanism within the proposed memetic algorithm.