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 …
Recent progress in organic Polymers-Composited sulfur materials as cathodes for Lithium-Sulfur battery
K Liu, H Zhao, D Ye, J Zhang - Chemical Engineering Journal, 2021 - Elsevier
With a theoretical specific capacity of 1675 mAh g− 1 and an energy density of 2600 W hg−
1, environmentally friendly lithium-sulfur batteries (LSBs) have been considered to be one of …
1, environmentally friendly lithium-sulfur batteries (LSBs) have been considered to be one of …
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 …
A correlation-guided layered prediction approach for evolutionary dynamic multiobjective optimization
When solving dynamic multiobjective optimization problems (DMOPs) by evolutionary
algorithms, the historical moving directions of some special points along the Pareto front …
algorithms, the historical moving directions of some special points along the Pareto front …
Multi-strategy dynamic multi-objective evolutionary algorithm with hybrid environmental change responses
A key issue in evolutionary algorithms for dynamic multi-objective optimization problems
(DMOPs) is how to detect and response environmental changes. Most existing evolutionary …
(DMOPs) is how to detect and response environmental changes. Most existing evolutionary …
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 …
Exploiting fractional accumulation and background value optimization in multivariate interval grey prediction model and its application
H Huang, Z Tao, J Liu, J Cheng, H Chen - Engineering Applications of …, 2021 - Elsevier
In the context of small sample and poor information, the data often change rapidly and
interact with multiple factors which make it a challenge to analyse and predict multivariate …
interact with multiple factors which make it a challenge to analyse and predict multivariate …
Interaction-based prediction for dynamic multiobjective optimization
Dynamic multiobjective optimization poses great challenges to evolutionary algorithms due
to the change of optimal solutions or Pareto front with time. Learning-based methods are …
to the change of optimal solutions or Pareto front with time. Learning-based methods are …
A particle swarm algorithm based on the dual search strategy for dynamic multi-objective optimization
Dynamic multi-objective optimization problems (DMOPs) have multiple objectives that need
to be optimized simultaneously, while the objectives and/or constraints may change with …
to be optimized simultaneously, while the objectives and/or constraints may change with …
A dynamic constrained multiobjective evolutionary algorithm based on decision variable classification
Y Guo, M Huang, G Chen, D Gong, J Liang… - Swarm and Evolutionary …, 2023 - Elsevier
In dynamic constrained multiobjective optimization problems (DCMOPs), dynamics may
arise from time-varying objective functions or/and constraints. To solve these problems …
arise from time-varying objective functions or/and constraints. To solve these problems …