In this article, a novel dynamic multiobjective optimization algorithm (DMOA) is proposed
based on a designed hierarchical response system (HRS). Named HRS-DMOA, the …

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

Dynamic multiobjective optimization poses great challenges to evolutionary algorithms due
to the change of optimal solutions or Pareto front with time. Learning-based methods are …

Dynamic multiobjective optimization problems (DMOPs) are widely encountered in real-
world applications and have received considerable attention in recent years. During the …

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

Dynamic Constrained Multiobjective Optimization Problems (DCMOPs) are characterized by
multiple conflicting optimization objectives and constraints that vary over time. The presence …

Dynamic constrained multiobjective optimization involves irregular changes in the
distribution of the true Pareto-optimal fronts, drastic changes in the feasible region caused …

This article suggests a new dynamic multiobjective evolutionary algorithm (DMOEA) with
Knowledge Transfer and Maintenance, called KTM-DMOEA, which aims to alleviate the …

Dynamic constrained multi-objective optimization problems (DCMOPs) involve objective
functions and constraints that vary over time, requiring optimization algorithms to track the …

Dynamic multi-objective optimization problems (DMOPs) have several conflicting and time-
varying objectives or constraints. To quickly follow the dynamical Pareto optimal front (POF) …