Evolutionary dynamic multi-objective optimisation: A survey
Evolutionary dynamic multi-objective optimisation (EDMO) is a relatively young but rapidly
growing area of investigation. EDMO employs evolutionary approaches to handle multi …
growing area of investigation. EDMO employs evolutionary approaches to handle multi …
A knowledge guided transfer strategy for evolutionary dynamic multiobjective optimization
Y Guo, G Chen, M Jiang, D Gong… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
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 …
optima closer to the true one as soon as possible once a new environment occurs. Previous …
Multidirectional prediction approach for dynamic multiobjective optimization problems
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 …
algorithms (EAs) to be able to rapidly track the moving Pareto front of an optimization …
A reinforcement learning approach for dynamic multi-objective 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 …
as a major real-world optimization problem receiving considerable attention. Tracking the …
Dynamic multiobjectives optimization with a changing number of objectives
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 …
dependent objective functions, while the ones with a changing number of objectives have …
Dynamic multi-objective evolutionary algorithm based on knowledge transfer
L Wu, D Wu, T Zhao, X Cai, L Xie - Information Sciences, 2023 - Elsevier
Dynamic multi-objective optimization problems (DMOPs) are mainly reflected in objective
changes with changes in the environment. To solve DMOPs, a transfer learning (TL) …
changes with changes in the environment. To solve DMOPs, a transfer learning (TL) …
Evolutionary dynamic database partitioning optimization for privacy and utility
Distributed database system (DDBS) technology has shown its advantages with respect to
query processing efficiency, scalability, and reliability. Moreover, by partitioning attributes of …
query processing efficiency, scalability, and reliability. Moreover, by partitioning attributes of …
A prediction strategy based on center points and knee points for evolutionary dynamic multi-objective optimization
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 …
time, requiring an optimization algorithm to track the movement of the Pareto front (Pareto …
A multimodel prediction method for dynamic multiobjective evolutionary optimization
M Rong, D Gong, W Pedrycz… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
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 …
problem (DMOP) with only one type of the Pareto set (PS) change. However, a continuous …
A domain adaptation learning strategy for dynamic multiobjective optimization
G Chen, Y Guo, M Huang, D Gong, Z Yu - Information Sciences, 2022 - Elsevier
Dynamic multiobjective optimization problems (DMOPs) require the robust tracking of Pareto-
optima varying over time. Previous transfer learning-based problem solvers consume the …
optima varying over time. Previous transfer learning-based problem solvers consume the …