The Improved epsilon-dominance Multi-objective Evolutionary Algorithm based on Rough Set Theory
基于粗糙集理论的改进ε-支配多目标进化算法*

The epsilon-MOEA, which based on the epsilon dominance, had the good performance on convergence and diversity, however, it also had some disadvantages. Such as, the value of epsilon was difficult to set, many extreme or representative individuals was easy to lose. In order to solve this problem, boundary region in rough set theory was introduced to improve the spread and quality of the initial approximation of the pareto front in epsilon-MOEA, then a improved epsilon-MOEA based on rough set theory is proposed. The experimental results illustrate epsilon-MOEA/RST has the good performance, which is much better at convergence and diversity than epsilon-MOEA.