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 …

When solving dynamic multiobjective optimization problems (DMOPs) by evolutionary
algorithms, the historical moving directions of some special points along the Pareto front …

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

Dynamic constrained multiobjective optimization problems (DCMOPs) abound in real-world
applications and gain increasing attention in the evolutionary computation community. To …

Dynamic multiobjective optimization problem (DMOP) denotes the multiobjective
optimization problem, which contains objectives that may vary over time. Due to the …

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

In this paper, a large-scale multi-objective gate assignment model is constructed by
considering the flight international and domestic attributes, task type, airline affiliation, and …

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

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