Uncertainty inadvertently exists in most real-world applications. In the optimization process,
uncertainty poses a very important issue and it directly affects the optimization performance …

Uncertainty is an important feature abstracted from real-world applications. Multiobjective
optimization problems (MOPs) with uncertainty can always be characterized as robust MOPs …

The multiobjective optimization problems (MOPs) under uncertain environments are very
challenging to be solved due to the sensitivities of some robust decision variables. To find …

Multi-Objective Optimization (MOO) problems might be subject to many modeling or
manufacturing uncertainties that affect the performance of the solutions obtained by a multi …

In optimization studies including multi-objective optimization, the main focus is usually
placed in finding the global optimum or global Pareto-optimal frontier, representing the best …

Multi-objective (MO) optimization is a challenging research topic because it involves the
simultaneous optimization of several complex and conflicting objectives that requires …

Many real-world applications involve the simultaneous optimization of several competing
objectives and are susceptible to decision or environmental parameter variation which …

Performance metrics are essential for quantifying the performance of optimization algorithms
in the field of evolutionary multi-objective optimization. Such metrics allow researchers to …

Dominance robustness (DR) has been proposed for assessing the ability of the Pareto-
optimal solutions to remain to be nondominated when the decision variables are subject to …

In real-world applications, it is often desired that a solution is not only of high performance,
but also of high robustness. In this context, a solution is usually called robust, if its …