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 …
A learnable population filter for dynamic multi-objective optimization
Z Fang, H Li, L Hu, N Zeng - Neurocomputing, 2024 - Elsevier
In this paper, a novel learnable population filter is proposed to solve the dynamic multi-
objective problems (DMOPs), whose main idea is to pick out potential valuable individuals in …
objective problems (DMOPs), whose main idea is to pick out potential valuable individuals in …
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 …
Individual-based transfer learning for dynamic multiobjective optimization
Dynamic multiobjective optimization problems (DMOPs) are characterized by optimization
functions that change over time in varying environments. The DMOP is challenging because …
functions that change over time in varying environments. The DMOP is challenging because …
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 …
Reducing negative transfer learning via clustering for dynamic multiobjective optimization
Dynamic multiobjective optimization problems (DMOPs) aim to optimize multiple (often
conflicting) objectives that are changing over time. Recently, there are a number of …
conflicting) objectives that are changing over time. Recently, there are a number of …
A framework based on historical evolution learning for dynamic multiobjective optimization
Dynamic multiobjective optimization problems (DMOPs) are widely encountered in real-
world applications and have received considerable attention in recent years. During the …
world applications and have received considerable attention in recent years. During the …
Improved population prediction strategy for dynamic multi-objective optimization algorithms using transfer learning
Many real-world optimization problems have dynamic multiple objectives and constrains,
such problems are called dynamic multi-objective optimization problems (DMOPs). Although …
such problems are called dynamic multi-objective optimization problems (DMOPs). Although …
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 fast dynamic evolutionary multiobjective algorithm via manifold transfer learning
Many real-world optimization problems involve multiple objectives, constraints, and
parameters that may change over time. These problems are often called dynamic …
parameters that may change over time. These problems are often called dynamic …