Complexity and variety of modern multiobjective optimisation problems result in the
emergence of numerous search techniques, from traditional mathematical programming to …

Interactive multiobjective optimization (IMO) aims at finding the most preferred solution of a
decision maker with the guidance of his/her preferences which are provided progressively …

Hypervolume is widely used as a performance indicator in the field of evolutionary
multiobjective optimization (EMO). It is used not only for performance evaluation of EMO …

Multiobjective evolutionary algorithms based on decomposition (MOEA/D) have attracted
tremendous attention and achieved great success in the fields of optimization and decision …

Protein structure prediction (PSP) problems are a major biocomputing challenge, owing to
its scientific intrinsic that assists researchers to understand the relationship between amino …

Previous methods of designing a bolt supporting network, which depend on engineering
experiences, seek optimal bolt supporting schemes in terms of supporting quality. The …

Conventional multiobjective optimization algorithms (MOEAs) with or without preferences
are successful in solving multi-and many-objective optimization problems. However, a …

As an essential component in multi-and many-objective optimization, decision-making
process either selects a subset of solutions from the whole Pareto front or guides the search …

The decomposition-based evolutionary algorithm has become an increasingly popular
choice for posterior multiobjective optimization. Facing the challenges of an increasing …

The ultimate goal of multiobjective optimization is to help a decision maker (DM) identify
solution (s) of interest (SOI) achieving satisfactory tradeoffs among multiple conflicting …