Multiobjective evolutionary algorithms (MOEAs) have been widely used in real-world
applications. However, most MOEAs based on Pareto-dominance handle many-objective …

In this work we present an overview of the most prominent population-based algorithms and
the methodologies used to extend them to multiple objective problems. Although not exact in …

Decomposition is a well-known strategy in traditional multiobjective optimization. However,
the decomposition strategy was not widely employed in evolutionary multiobjective …

Decomposition via scalarization is a basic concept for multiobjective optimization. The
weighted sum (WS) method, a frequently used scalarizing method in decomposition-based …

Decomposition-based evolutionary algorithms have been quite successful in solving
optimization problems involving two and three objectives. Recently, there have been some …

It is known that Pareto dominance has its own weaknesses as the selection criterion in
evolutionary multiobjective optimization. Algorithms based on Pareto criterion (PC) can …

Decomposition-based algorithms have become increasingly popular for evolutionary
multiobjective optimization. However, the effect of scalarizing methods used in these …

We examine the behavior of three classes of evolutionary multiobjective optimization (EMO)
algorithms on many-objective knapsack problems. They are Pareto dominance-based …

Multiobjective evolutionary algorithms based on decomposition (MOEA/D) decompose a
multiobjective optimization problem into a set of simple optimization subproblems and solve …

Combining ant colony optimization (ACO) and the multiobjective evolutionary algorithm (EA)
based on decomposition (MOEA/D), this paper proposes a multiobjective EA, ie, MOEA/D …