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 …
Maintenance applications of multi-criteria optimization: A review
CS Syan, G Ramsoobag - Reliability Engineering & System Safety, 2019 - Elsevier
Modern-day maintenance optimization decisions are complex problems which need to
satisfy multiple and conflicting criteria. With increased applications and recent advances in …
satisfy multiple and conflicting criteria. With increased applications and recent advances in …
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 …
A survey of evolutionary continuous dynamic optimization over two decades—Part B
This article presents the second Part of a two-Part survey that reviews evolutionary dynamic
optimization (EDO) for single-objective unconstrained continuous problems over the last two …
optimization (EDO) for single-objective unconstrained continuous problems over the last two …
A survey of evolutionary continuous dynamic optimization over two decades—Part A
Many real-world optimization problems are dynamic. The field of dynamic optimization deals
with such problems where the search space changes over time. In this two-part article, we …
with such problems where the search space changes over time. In this two-part article, we …
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 …
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 …
[PDF][PDF] 多目标进化算法性能评价指标研究综述
王丽萍， 任宇， 邱启仓， 邱飞岳 - 计算机学报, 2021 - 159.226.43.17
多目标进化算法性能评价指标研究综述 Page 1 第??卷第?期 计算机学报 Vol. ?? No. ? 20??年
?月 CHINESE JOURNAL OF COMPUTERS ???. 20?? 收稿日期:年-月-日;最终修改稿收到日期 …
?月 CHINESE JOURNAL OF COMPUTERS ???. 20?? 收稿日期:年-月-日;最终修改稿收到日期 …
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 …