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 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 …
Evolutionary dynamic multiobjective optimization assisted by a support vector regression predictor
L Cao, L Xu, ED Goodman, C Bao… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
Dynamic multiobjective optimization problems (DMOPs) challenge multiobjective
evolutionary algorithms (MOEAs) because those problems change rapidly over time. The …
evolutionary algorithms (MOEAs) because those problems change rapidly over time. The …
A novel dynamic multiobjective optimization algorithm with hierarchical response system
In this article, a novel dynamic multiobjective optimization algorithm (DMOA) is proposed
based on a designed hierarchical response system (HRS). Named HRS-DMOA, the …
based on a designed hierarchical response system (HRS). Named HRS-DMOA, the …
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 …
Dynamic multi-objective AWPSO in DT-assisted UAV cooperative task assignment
M Deng, Z Yao, X Li, H Wang… - IEEE Journal on …, 2023 - ieeexplore.ieee.org
In recent years, more and more attention has been paid to the unmanned aerial vehicle
(UAV) cooperative task assignment. In order to complete the task with the lowest cost, some …
(UAV) cooperative task assignment. In order to complete the task with the lowest cost, some …
Dynamic adaptive multi-objective optimization algorithm based on type detection
Dynamic multi-objective optimization problems (DMOPs) are multi-objective problems that
are influenced by dynamically changing environmental parameters. Most current algorithms …
are influenced by dynamically changing environmental parameters. Most current algorithms …
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 …