Over the past decades, evolutionary multi-objective optimization algorithms have shown their strength on solving the multi-objective optimization problems. The S-Metric Selection Evolutionary Multiobjective Optimization Algorithm (SMS-EMOA) is a state-of-the-art algorithm which uses the hypervolume indicator as selection criterion and performs well in finding well distributed solutions to approximate the Pareto front. In this paper, the concept of multiple dynamic reference points is integrated to the SMS-EMOA which can balance the trade-off of exploration and exploitation by changing the number of reference points. This way it combines concepts of indicator based and decomposition based EMOA design in a promising manner. The proposed algorithm is compared with other well established EMOA on the classical ZDT benchmark which have 5 different test problems with two objectives. The results show that SMS-EMOA with multiple dynamic reference points outperforms other state-of-the-art algorithms, including SMS-EMOA, by covering more hypervolume which means it has a better approximation of the Pareto front.
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