Land use optimization is a promising approach to achieve urban sustainability. Despite the
increasing number of literature on land use optimization, a little investigation is made to …

Over the last three decades, a large number of evolutionary algorithms have been
developed for solving multi-objective optimization problems. However, there lacks an upto …

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

The application of multiobjective evolutionary algorithms to many-objective optimization
problems often faces challenges in terms of diversity and convergence. On the one hand …

Multiobjective optimization evolutionary algorithms (MOEAs) have received significant
achievements in recent years. However, they encounter many difficulties in dealing with …

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

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

When optimizing many-objective problems (MaOP), the same strategy might behave
differently when facing problems with different features. Therefore, obtaining problem …

Interval many-objective optimization problems (IMaOPs), involving more than three
objectives and at least one subjected to interval uncertainty, are ubiquitous in real-world …

Pareto dominance based approach is a classical method for solving multi-objective
optimization problems (MOPs). However, as the number of objectives increases, the …