Many-objective optimization problems abbreviated as MaOPs with more than three
objectives have attracted increasing interests due to their widely existing in a variety of real …

Real-world problems which involve the optimization of multiple conflicting objectives are
named as multi-objective optimization problems (MOPs). This paper mainly deals with the …

For many-objective optimization problems (MaOPs), the proportion of non-dominated
solutions in a population scales up sharply with the increase in the number of objectives …

Evolutionary multi-objective optimization methods have become increasingly popular in
finding a representative set of Pareto optimal solutions for multi-objective optimization …

In recent years, a general-purpose local-search heuristic method called Extremal
Optimization (EO) has been successfully applied in some NP-hard combinatorial …

The main issues for the optimization of many-objective evolutionary are about two aspects:
the balance between convergence and diversity, and increasing the selection pressure …

Due to the curse of dimensionality, the existing evolutionary algorithms have difficulties in
balancing convergence and diversity in many-objective problems. To address this …

The current many-objective evolutionary algorithms (MaOEAs) generally adopt the mutation
strategies designed for single-objective optimization problems directly. However, these …

Many-objective optimization problems are common in real-world applications, few
evolutionary optimization methods, however, are suitable for solving them up to date due to …

As a recently developed evolutionary algorithm inspired by far-from-equilibrium dynamics of
self-organized criticality, extremal optimization (EO) has been successfully applied to a …