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 …

Dynamic multiobjective optimization problems exist widely in the real word and require the
optimization algorithms to track the Pareto front (PF) over time. A prediction strategy based …

In real life, there are many dynamic multi-objective optimization problems which vary over
time, requiring an optimization algorithm to track the movement of the Pareto front (Pareto …

This paper presents a novel population prediction algorithm based on modular neural
network (PA-MNN) for handling dynamic multi-objective optimization. The proposed …

Dynamic multiobjective optimization problems (DMOPs) require Evolutionary algorithms
(EAs) to track the time-dependent Pareto-optimal front (PF) or Pareto-optimal set (PS), and …

Responding to environmental changes quickly is a very key component in solving dynamic
multi-objective optimization problems (DMOPs). Most existing methods perform well on …

Prediction strategies are widely-used in dynamic multi-objective evolutionary algorithms
(DMOEAs). However, the characteristics of the environmental changes are different and only …

In practical applications and daily life, dynamic multiobjective optimization problems
(DMOPs) are ubiquitous. The purpose of dealing with DMOPs is to track moving Pareto Front …

Dynamic multiobjective optimization problems exist in daily life and industrial practice. The
objectives of dynamic multiobjective optimization problems conflict with each other. In most …

This paper proposes a new dynamic multi-objective optimization algorithm by integrating a
new fitting-based prediction (FBP) mechanism with regularity model-based multi-objective …