Handling dynamic multiobjective optimization environments via layered prediction and subspace-based diversity maintenance
In this article, we propose an evolutionary algorithm based on layered prediction (LP) and
subspace-based diversity maintenance (SDM) for handling dynamic multiobjective …
subspace-based diversity maintenance (SDM) for handling dynamic multiobjective …
A mahalanobis distance-based approach for dynamic multiobjective optimization with stochastic changes
In recent years, researchers have made significant progress in handling dynamic
multiobjective optimization problems (DMOPs), particularly for environmental changes with …
multiobjective optimization problems (DMOPs), particularly for environmental changes with …
Combining a hybrid prediction strategy and a mutation strategy for dynamic multiobjective optimization
The environments of the dynamic multiobjective optimization problems (DMOPs), such as
Pareto optimal front (POF) or Pareto optimal set (POS), usually frequently change with the …
Pareto optimal front (POF) or Pareto optimal set (POS), usually frequently change with the …
A dynamic multi-objective optimization evolutionary algorithm for complex environmental changes
R Liu, P Yang, J Liu - Knowledge-Based Systems, 2021 - Elsevier
Dynamic multi-objective optimization problems (DMOPs) have attracted more and more
research in the field of evolutionary computation community in recent years. Unlike most …
research in the field of evolutionary computation community in recent years. Unlike most …
Elitism-based transfer learning and diversity maintenance for dynamic multi-objective optimization
In handling dynamic multi-objective optimization problems (DMOPs), transfer learning driven
methods have received considerable attention for finding a high-quality initial population …
methods have received considerable attention for finding a high-quality initial population …
Multiregional co-evolutionary algorithm for dynamic multiobjective optimization
X Ma, J Yang, H Sun, Z Hu, L Wei - Information Sciences, 2021 - Elsevier
Dynamic multiobjective optimization problems (DMOPs) require Evolutionary algorithms
(EAs) to track the time-dependent Pareto-optimal front (PF) or Pareto-optimal set (PS), and …
(EAs) to track the time-dependent Pareto-optimal front (PF) or Pareto-optimal set (PS), and …
Interindividual correlation and dimension-based dual learning for dynamic multiobjective optimization
Dynamic multiobjective optimization problems (DMOPs) are characterized by their multiple
objectives, constraints, and parameters that may change over time. The challenge in solving …
objectives, constraints, and parameters that may change over time. The challenge in solving …
A dynamic multi-objective optimization based on a hybrid of pivot points prediction and diversity strategies
There are many dynamic multi-objective optimization problems (DMOPs) in real-world
applications. The Pareto-optimal front (PF) or Pareto-optimal set (PS) of such problems will …
applications. The Pareto-optimal front (PF) or Pareto-optimal set (PS) of such problems will …
A dynamic multi-objective evolutionary algorithm using adaptive reference vector and linear prediction
Responding to environmental changes quickly is a very key component in solving dynamic
multi-objective optimization problems (DMOPs). Most existing methods perform well on …
multi-objective optimization problems (DMOPs). Most existing methods perform well on …
A population diversity maintaining strategy based on dynamic environment evolutionary model for dynamic multiobjective optimization
Z Peng, J Zheng, J Zou - 2014 IEEE Congress on Evolutionary …, 2014 - ieeexplore.ieee.org
Maintaining population diversity is a crucial issue for the performance of dynamic
multiobjective optimization algorithms. However traditional dynamic multiobjective …
multiobjective optimization algorithms. However traditional dynamic multiobjective …