In this paper, an interactive version of the decomposition based multiobjective evolutionary
algorithm (iMOEA/D) is proposed for interaction between the decision maker (DM) and the …

Decomposition has become an increasingly popular technique for evolutionary
multiobjective optimization (EMO). A decomposition-based EMO algorithm is usually …

Evolutionary multi-objective optimization (EMO) methodologies have been widely applied to
find a well-distributed trade-off solutions approximating to the Pareto-optimal front in the past …

While traditional Pareto-based evolutionary multi-objective optimization (EMO) algorithms
have shown an excellent balance between convergence and diversity on a wide range of …

Preference information (such as the reference point) of the decision maker (DM) is often
used in multiobjective optimization; however, the location of the specified reference point …

The issue of integrating preference information into multi-objective optimization is
considered, and a multi-objective framework based on decomposition and preference …

Multi-objective evolutionary algorithms based on decomposition (MOEA/D) decomposes a
multi-objective optimization problem (MOP) into a set of simple scalar objective optimization …

This paper presents a new Multiobjective Evolutionary Algorithm (MOEA) based on
decomposition, with fuzzy dominance (MOEA/DFD). The algorithm introduces a fuzzy Pareto …

Multi-objective optimization is an essential and challenging topic in the domains of
engineering and computation because real-world problems usually include several …

Recently, multi-objective evolutionary algorithms (MOEAs) have been widely explored and
applied to many real-world problems. Particularly, preference-based MOEAs are among the …