A bilevel coevolution framework with knowledge transfer for large-scale optimization and its application in multiperiod economic dispatch
A Pan, H Liu, Y Shan, B Shen - Engineering Applications of Artificial …, 2025 - Elsevier
Complex systems typically consist of multiple components and serve requirements across
multiple periods. Their optimization involves large-scale parameters. If all parameters are …
multiple periods. Their optimization involves large-scale parameters. If all parameters are …
Boosting scalability for large-scale multiobjective optimization via transfer weights
Large-scale multiobjective optimization problems (LSMOPs), which optimize multiple
conflicting objectives with hundreds or even thousands of decision variables, demand …
conflicting objectives with hundreds or even thousands of decision variables, demand …
An improved problem transformation algorithm for large-scale multi-objective optimization
Y Sun, D Jiang - Swarm and Evolutionary Computation, 2024 - Elsevier
Abstract Solving Large-Scale Multi-Objective Optimization Problems (LSMOPs) is the major
challenge in evolution computation. Due to the large number of decision variables involved …
challenge in evolution computation. Due to the large number of decision variables involved …
Dynamic Modeling and Solving Methods for Multi-Train Energy-Efficient Operation and Network Voltage Stability
X Tao, C Fu, Z Xiao, Q Wang, X Feng… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Freight trains operate in dynamic environments and exhibit time-varying behavior, making
static mechanistic models inadequate for capturing these changes. This often results in …
static mechanistic models inadequate for capturing these changes. This often results in …
Heuristic Initialization and Knowledge-based Mutation for Large-Scale Multi-Objective 0-1 Knapsack Problems
Recently, there has been a growing interest in large-scale multiobjective optimization
problems within the evolutionary multiobjective optimization (EMO) community. These …
problems within the evolutionary multiobjective optimization (EMO) community. These …
Performance evaluation of multi-objective evolutionary algorithms using artificial and real-world problems
Performance of evolutionary multi-objective optimization (EMO) algorithms is usually
evaluated using artificial test problems such as DTLZ and WFG. Every year, new EMO …
evaluated using artificial test problems such as DTLZ and WFG. Every year, new EMO …
Coarse-to-fine evolutionary search for large-scale multi-objective optimization: An application to ratio error estimation of voltage transformers
J Li, K Zou, L Xing - Frontiers in Energy Research, 2022 - frontiersin.org
Multi-objective optimization problems (MOPs) are commonly confronted in various fields,
such as condition monitoring for renewable energy systems, and ratio error estimation of …
such as condition monitoring for renewable energy systems, and ratio error estimation of …
Joint Delivery Path Optimization Based on Improved Particle Swarm Optimization Algorithm
T Luo, Y Xu, L Xing, J Li - 2023 8th International Conference on …, 2023 - ieeexplore.ieee.org
Aiming at the characteristics of low transportation efficiency and high transportation cost in
the joint delivery path optimization problem, a mathematical optimization model is …
the joint delivery path optimization problem, a mathematical optimization model is …
Privacy-Preserving Task Offloading in Vehicular Edge Computing
B Cao, Z Li, X Liu, Z Lv - 2023 Asia Symposium on Image …, 2023 - ieeexplore.ieee.org
With the development of mobile edge computing (MEC), MEC-based task offloading of
vehicle edge computing (VEC) has become popular due to its low delay. However, when …
vehicle edge computing (VEC) has become popular due to its low delay. However, when …