I. Introduction
Many real-world optimization problems have multiple conflicting objectives, to which a set of Pareto-optimal solutions will be found [1]. Without loss of generality, a multiobjective optimization problem (MOP) can be formulated as the following -objective minimization problem: \begin{equation*} \textrm {minimize } \digamma \left ({{x}}\right)=\left ({f_{1}\left ({{x}}\right),\ldots f_{m}\left ({{x}}\right)}\right)^{T}\tag{1}\end{equation*} where is the decision vector. , where and are the lower and upper bounds, respectively, of the th decision variable. is the decision space, and is the number of decision variables. consists of objectives. When is larger than three, the MOP is also known as a many-objective optimization problem (MaOP).