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

To solve dynamic multi-objective optimization problems, evolutionary algorithms must be
capable of quickly and accurately tracking the changing Pareto front such that they can …

In recent years, an increasing number of prediction-based strategies have shown promising
results in handling dynamic multi-objective optimization problems (DMOPs), and prediction …

The prediction strategy is a key method for solving dynamic multi-objective optimization
problems (DMOPs), particularly the commonly used linear prediction strategy, which has an …

In this paper, we address the prize-collecting generalized minimum spanning tree problem
(PC-GMSTP) which aims to find a minimum spanning tree to connect a network of clusters …

The dynamic multi-objective optimization problem is a common problem in real life, which is
characterized by conflicting objectives, the Pareto frontier (PF) and Pareto solution set (PS) …

In the dynamic multi-objective optimization problems, if the environmental changes are
detected, an appropriate response strategy be employed to respond quickly to the change …

Solving dynamic multi-objective optimization problems with time-varying Pareto front (PF) or
Pareto set (PS) is a challenging task. Such problems require algorithms to react to …

Abstracts In dynamic multi-objective optimization problems (DMOPs), objective functions,
problem parameters, and constraints may change over time. Mainly, DMOPs use response …

Dynamic interval multi-objective optimization problems are a class of optimization problems
whose interval parameters change with the environment. However, the existing algorithms …