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 …

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

Dynamic multiobjective optimization problems (DMOPs) are characterized by a time-variant
Pareto optimal front (PF) and/or Pareto optimal set (PS). To handle DMOPs, an algorithm …

Dynamic multiobjective optimization problems (DMOPs) vary over time, requiring an
optimization algorithm to track the position of Pareto-optimal front (PF) in a dynamic …

The most obvious characteristic of dynamic multi-objective optimization problems (DMOPs)
is the time-varying Pareto-optimal set (POS) or/and Pareto-optimal front (POF). This kind of …

In reality, many multi-objective optimization problems are dynamic. The Pareto optimal front
(PF) or Pareto optimal solution (PS) of these dynamic multi-objective problems (DMOPs) …

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

This paper presents a novel prediction model combined with a multiobjective evolutionary
algorithm based on decomposition to solve dynamic multiobjective optimization problems. In …

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 …