Multi-objective optimization problem (MOOP) differs from single-objective optimization problem (SOOP) in the number of objective functions and the way solutions are compared [14].
SOOP typically has a single optimal solution in the solution space, and solutions are compared based on their fitness value.
On the other hand, MOOPs have a set of optimal solutions which are known as Pareto front solutions [14, 15]. This set of solutions cannot be dominated by other solutions in the search space and hence termed as non-dominated set of solutions.
Generally, a Pareto front corresponds to a curve or an extremely complex hypersurface. MOOPs have many challenges like time complexity and dimensionality.
