KDT-MOEA: A multiobjective optimization framework based on K-D trees - Lacerda, Allysson S.M.; Batista, Lucas S. (Information sciences, 2019)
This paper presents KDT-MOEA, a framework that takes advantage of a special kind of binary search tree, named K-D tree, to solve multiobjective optimization problems (MOPs). Our main goal is to explore the capabilities of this data structure to define neighborhood structures either in decision variables space or in objective space, as well as by switching between them at any time. The KDT-MOEA framework performance is compared with five state-of-the-art algorithms on the DTLZ, WFG and LZ09 benchmarking problems with up to 15 objectives. Statistical tests demonstrate that KDT-MOEA was able to outperform the compared methods on most problems. In addition, in order to evaluate the flexibility and the potential of the proposed operators, extended versions of the compared algorithms are also presented. Empirical results pointed out that the new versions were superior to all five original MOEAs, indicating that the proposed operators can also be easily incorporated into existing MOEAs with different strategies to achieve better results.