The current literature of evolutionary many-objective optimization is merely focused on the
scalability to the number of objectives, while little work has considered the scalability to the …

Since non-dominated sorting was first adopted in NSGA in 1995, most evolutionary
algorithms have employed non-dominated sorting as one of the major criteria in their …

In multi-objective evolutionary algorithms (MOEAs), non-domina-ted sorting is one of the
critical steps to locate efficient solutions. A large percentage of computational cost of MOEAs …

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

In recent years, multiobjective immune algorithms (MOIAs) have shown promising
performance in solving multiobjective optimization problems (MOPs). However, basic MOIAs …

Evolutionary computation has shown great performance in solving many multi-objective
optimization problems; in many such algorithms, non-dominated sorting plays an important …

Non-dominated sorting is a technique often used in evolutionary algorithms to determine the
quality of solutions in a population. The most common algorithm is the Fast Non-dominated …

The difficulty of solving a multi-objective optimization problem is impacted by the number of
objectives to be optimized. The presence of many objectives typically introduces a number …

Non-dominated sorting has widely been adopted and shown to be very effective in
dominance based evolutionary multi-objective optimization where the number of objectives …

In this paper, we propose a new method called ND-Tree-based update (ND-Tree) for the
dynamic nondominance problem, ie, the problem of online update of a Pareto archive …