Although the trade-off between various optimization objectives can be elucidated through Pareto method, the large number of solutions in the Pareto front poses a challenge in selecting the most appropriate or satisfactory option for experimental verification and practical implementation . To address this issue, Wang et al. deployed fuzzy C-means clustering algorithm to narrow down the solutions on the Pareto front to several representative ones, see Fig. ). The fuzzy C-means clustering algorithm partitioned the Pareto solutions into 9 clusters and 9 representative solutions were selected based on the 2-norm distance to cluster centres for experimental verification. In addition to clustering, another method is to deploy multi-criteria decision making (MCDM) algorithm for ranking Pareto solutions . Liu et al. deployed the Technique for Order