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 …
[PDF][PDF] 动态多目标优化研究综述
刘若辰， 李建霞， 刘静， 焦李成 - 计算机学报, 2020 - cjc.ict.ac.cn
摘要现实生活中, 存在许多动态多目标优化问题(DynamicMulti
objectiveOptimizationProblems, DMOPs), 这类问题的目标函数之间相互矛盾, 并且目标函数 …
objectiveOptimizationProblems, DMOPs), 这类问题的目标函数之间相互矛盾, 并且目标函数 …
A novel dynamic multiobjective optimization algorithm with non-inductive transfer learning based on multi-strategy adaptive selection
In this article, a novel multi-strategy adaptive selection-based dynamic multiobjective
optimization algorithm (MSAS-DMOA) is proposed, which adopts the non-inductive transfer …
optimization algorithm (MSAS-DMOA) is proposed, which adopts the non-inductive transfer …
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 …
Transfer learning-based dynamic multiobjective optimization algorithms
One of the major distinguishing features of the dynamic multiobjective optimization problems
(DMOPs) is that optimization objectives will change over time, thus tracking the varying …
(DMOPs) is that optimization objectives will change over time, thus tracking the varying …
Knee point-based imbalanced transfer learning for dynamic multiobjective optimization
Dynamic multiobjective optimization problems (DMOPs) are optimization problems with
multiple conflicting optimization objectives, and these objectives change over time. Transfer …
multiple conflicting optimization objectives, and these objectives change over time. Transfer …
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 …
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 similarity-based cooperative co-evolutionary algorithm for dynamic interval multiobjective optimization problems
D Gong, B Xu, Y Zhang, Y Guo… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
Dynamic interval multiobjective optimization problems (DI-MOPs) are very common in real-
world applications. However, there are few evolutionary algorithms (EAs) that are suitable …
world applications. However, there are few evolutionary algorithms (EAs) that are suitable …
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 …