Most existing evolutionary search strategies are not so efficient when directly handling the
decision space of large-scale multiobjective optimization problems (LMOPs). To enhance …

Recent decades have witnessed great advancements in multiobjective evolutionary
algorithms (MOEAs) for multiobjective optimization problems (MOPs). However, these …

This paper presents trajectory tracking control of a snake robot in the presence of system
uncertainty such as disturbances and parameter uncertainties. First, we derive a realistic …

Innovization is a task of learning common relationships among some or all of the Pareto-
optimal (PO) solutions in multi-and many-objective optimization problems. A recent study …

Owing to the stochasticity of Evolutionary Multi-objective Optimization (EMO) Algorithms and
an application with a limited budget of solution evaluations, a perfectly converged and …

Since large-scale multi-objective problems (LSMOPs) have huge decision variables, the
traditional evolutionary algorithms are facing difficulties of low exploitation efficiency and …

When using evolutionary algorithms to solve constrained multi-objective optimization
problems, both the constraint handling technique and the search operator play a crucial role …

Complex systems typically consist of multiple components and serve requirements across
multiple periods. Their optimization involves large-scale parameters. If all parameters are …

In recent years, dynamic multiobjective evolutionary algorithms (DMOEAs) using the
prediction strategy have shown promising performance for solving dynamic multiobjective …

This paper proposes a machine learning (ML) based unified innovized progress (UIP)
operator to simultaneously enhance the convergence and diversity capabilities of reference …