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 survey on learnable evolutionary algorithms for scalable multiobjective optimization
Recent decades have witnessed great advancements in multiobjective evolutionary
algorithms (MOEAs) for multiobjective optimization problems (MOPs). However, these …
algorithms (MOEAs) for multiobjective optimization problems (MOPs). However, these …
Ensemble prediction-based dynamic robust multi-objective optimization methods
Y Guo, H Yang, M Chen, J Cheng, D Gong - Swarm and evolutionary …, 2019 - Elsevier
Many real-world multi-objective optimization problems are subject to environmental changes
over time, resulting in changing Pareto-optima. Wide studies on solving dynamic multi …
over time, resulting in changing Pareto-optima. Wide studies on solving dynamic multi …
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 …
A dynamic multi-objective optimization evolutionary algorithm based on particle swarm prediction strategy and prediction adjustment strategy
P Wang, Y Ma, M Wang - Swarm and evolutionary computation, 2022 - Elsevier
Abstract Dynamic Multi-objective Optimization Evolutionary Algorithm (DMOEA) is a
promising approach for solving Dynamic Multi-objective Optimization Problems (DMOPs) …
promising approach for solving Dynamic Multi-objective Optimization Problems (DMOPs) …
[HTML][HTML] Revisiting spatial optimization in the era of geospatial big data and GeoAI
Spatial optimization is an interdisciplinary field dedicated to the scientific and rational
allocation of resources spatially, which has received tremendous attention across various …
allocation of resources spatially, which has received tremendous attention across various …
Interaction-based prediction for dynamic multiobjective optimization
Dynamic multiobjective optimization poses great challenges to evolutionary algorithms due
to the change of optimal solutions or Pareto front with time. Learning-based methods are …
to the change of optimal solutions or Pareto front with time. Learning-based methods are …
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 …
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 …