Evolutionary dynamic multi-objective optimisation (EDMO) is a relatively young but rapidly
growing area of investigation. EDMO employs evolutionary approaches to handle multi …

The key task in dynamic multiobjective optimization problems (DMOPs) is to find Pareto-
optima closer to the true one as soon as possible once a new environment occurs. Previous …

Various real-world multiobjective optimization problems are dynamic, requiring evolutionary
algorithms (EAs) to be able to rapidly track the moving Pareto front of an optimization …

Abstract Dynamic Multi-objective Optimization Problem (DMOP) is emerging in recent years
as a major real-world optimization problem receiving considerable attention. Tracking the …

Existing studies on dynamic multiobjective optimization (DMO) focus on problems with time-
dependent objective functions, while the ones with a changing number of objectives have …

Dynamic multi-objective optimization problems (DMOPs) are mainly reflected in objective
changes with changes in the environment. To solve DMOPs, a transfer learning (TL) …

Distributed database system (DDBS) technology has shown its advantages with respect to
query processing efficiency, scalability, and reliability. Moreover, by partitioning attributes of …

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 …

A large number of prediction strategies are specific to a dynamic multiobjective optimization
problem (DMOP) with only one type of the Pareto set (PS) change. However, a continuous …

Dynamic multiobjective optimization problems (DMOPs) require the robust tracking of Pareto-
optima varying over time. Previous transfer learning-based problem solvers consume the …