The convergence and diversity enhancement of multiobjective evolutionary algorithms
(MOEAs) to efficiently solve many-objective optimization problems (MaOPs) is an active …

An important issue in hypervolume-based evolutionary multiobjective optimization (EMO)
algorithms is the specification of a reference point for hypervolume calculation. However, its …

Multiobjective evolutionary algorithms (MOEAs) have faced the challenge of balancing
diversity and convergence in dealing with many-objective optimization problems (MaOPs) …

The primary objective of multi-objective evolutionary algorithms (MOEAs) is to find a set of
evenly distributed nondominated solutions that approximate the Pareto front (PF) of a multi …

Evolutionary algorithms (EAs) are widely employed in multi-objective optimization (MOO) to
find a well-distributed set of near-Pareto solutions. Among various types of practicalities that …

In multi/many-objective optimization, a decision maker (DM) may often be interested in
examining only a small set of solutions instead of the entire Pareto optimal front (PF). Such …

Context. This paper deals with bilateral joint decision making in supply chains, and more
specifically focuses on coordinating the decisions taken by the supplier and the producer in …

In high-dimensional space, most multi-objective optimization algorithms encounter
difficulties in solving many-objective optimization problems because they cannot balance …