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

Performing an accurate clustering analysis is of great significance for us to understand the
behavior of complex networks, and a variety of graph clustering algorithms have, thus, been …

Since different features may require different costs, the cost-sensitive feature selection
problem become more and more important in real-world applications. Generally, it includes …

Recently, it was found that most multiobjective particle swarm optimizers (MOPSOs) perform
poorly when tackling many-objective optimization problems (MaOPs). This is mainly …

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 …

Recently, multi-objective particle swarm optimization (MOPSO) has shown the effectiveness
in solving multi-objective optimization problems (MOPs). However, most MOPSO algorithms …

The job-shop scheduling problem (JSSP) is a challenging scheduling and optimization
problem in the industry and engineering, which relates to the work efficiency and operational …

A large number of prediction strategies are specific to a dynamic multiobjective optimization
problem (DMOP) with only one type of the Pareto set (PS) change. However, a continuous …

The bias feature is a major factor that makes a multiobjective optimization problem (MOP)
difficult for multiobjective evolutionary algorithms (MOEAs). To deal with this problem …

The selection of swarm leaders (ie, the personal best and global best), is important in the
design of a multiobjective particle swarm optimization (MOPSO) algorithm. Such leaders are …