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Figures 13 and 14 show the MOEA simulation results for the case of an infeasible goal setting with soft and hard priorities, respectively. In the figures, diamonds represent goals, small circles represent non-dominated individuals and solid dots represent dominated individuals. For the soft priority setting in Figure 13, goals are treated as first priority followed by the objective component of f1 as second priority, i.e., Pg = [1, 1] and Pf **= [2, 0]. As can be seen, it provides a distributive** 
optimization approach for all goals by pushing the population towards the objective component of f1 **that has a higher priority, after taking the goal vector into consideration (c.f. Figures 3a, 13b). In** contrast, Figure 14 shows the minimization results with hard priority setting where priority of f1 **is** 
higher than f2, i.e., Pg = [1, 2] and Pf **= [0, 0]. Unlike the soft priority optimization, hard priority** 
minimizes the objective of f1 until the relevant goal component of g1 **= 0.5 is satisfied before** attaining the objective component of f2 with the second goal component of g2 **= 0.5, as shown in** 
Figure 14 (c.f. Figures 3b, 14b). As can be seen, objective values with hard priority settings are better with higher priority but are worse with lower priority, as compared to the solutions obtained in soft priority optimization (c.f. Figures 13b, 14b). 

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