The challenge of solving dynamic multi-objective optimization problems is to effectively and
efficiently trace the varying Pareto optimal front and/or Pareto optimal set. To this end, this …

Prediction methods are useful tools for dynamic multiobjective optimization (DMO),
especially if the changes roughly follow some patterns. Multi-model prediction methods, in …

When solving dynamic multiobjective optimization problems, most evolutionary algorithms
attempt to predict the initial population in a new environment by mining the relationships …

In recent years, dynamic multiobjective optimization problems (DMOPs) have drawn
increasing interest. Many dynamic multiobjective evolutionary algorithms (DMOEAs) have …

In dynamic multiobjective optimization, the Pareto front (PF) or Pareto set varies over time as
the problem environment changes. In such scenarios, optimization algorithms are required …

Recently, dynamic multiobjective evolutionary algorithms (DMOEAs) with transfer learning
have become popular for solving dynamic multiobjective optimization problems (DMOPs), as …

After detecting the change of the environment, it is effective to respond to the change of the
environment. However, the majorities of these methods only respond to the change of the …

Dynamic multi-objective optimization problems (DMOPs) require an algorithm to track the
true Pareto-optimal front (POF) or Pareto-optimal set (POS) quickly and accurately when the …

In this article, we propose an evolutionary algorithm based on layered prediction (LP) and
subspace-based diversity maintenance (SDM) for handling dynamic multiobjective …

Dynamic multiobjective optimization problems (DMOPs) are characterized by their multiple
objectives, constraints, and parameters that may change over time. The challenge in solving …