Multi-objective programming is commonly used in the literature when conflicted objectives
arise in solving optimization problems. Over the past decades, classical optimization …

This systematic review aims to evaluate the efficacy of fuzzy multi-objective optimization
techniques in supporting decision-making in financial portfolio management under …

Multi-objective optimization under uncertainty has gained considerable attention in recent
years due to its practical applications in real-life. Many studies have been conducted on this …

Multi-objective programming (MOP) can simultaneously optimize multi-objectives in
mathematical programming models, but the optimization of multi-objectives triggers the …

Everyday people need to make decisions, and the decision makers usually face multiple,
conflicting objectives and uncertain environments. The research about uncertain multi …

In large-scale multiobjective optimization, too many decision variables hinder the
convergence search of evolutionary algorithms. Reducing the search range of the decision …

Optimization is a widely used technique in Operational Research that has been employed in
a range of applications. The aim is to maximize or minimize a function (eg. maximizing profit …

Multi-objective optimization (MO) is a fast-developing field in computational intelligence
research. Giving decision makers more options to choose from using some post-analysis …

In large-scale multiobjective optimization, the huge search space poses a great challenge to
the convergence search of existing evolutionary algorithms. A fuzzy decision variables …

In fuzzy optimization it is desirable that all fuzzy solutions under consideration be attainable,
so that the decision maker will be able to make “a posteriori” decisions according to current …