Multi-objective optimization problems having more than three objectives are referred to as
many-objective optimization problems. Many-objective optimization brings with it a number …

Given the global economic and societal importance of the polymer industry, the continuous
search for improvements in the various processing techniques is of practical primordial …

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

Robust multi-objective optimization problems (RMOPs) widely exist in real-world
applications, which introduce a variety of uncertainty in optimization models. While some …

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 …

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

In this paper, interval sequential linear programming (ISLP) is proposed to solve nonlinear
robust optimization (RO). The main idea of the programming is to transform the uncertain …

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 …

Solving many-objective problems (MaOPs) is still a significant challenge in the multi-
objective optimization (MOO) field. One way to measure algorithm performance is through …

Accuracy and diversity are two vital requirements for constructing classifier ensembles.
Previous work has achieved this by sequentially selecting accurate ensemble members …