Evolutionary multi-task optimization with hybrid knowledge transfer strategy

Cai, Yiqiao; Peng, Deming; Liu, Peizhong; Guo, Jing-Ming

doi:10.1016/j.ins.2021.09.021

Cited by 26 publications

(4 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This section compares the proposed HPIM-KTSMOEA algorithm with the knowledge transfer for multi-objective evolutionary algorithm, (KT-DMOEA [37]), bidirectional knowledge transfer for MOEA algorithm (BKT-MOEA [38]), hybrid knowledge transfer for MOEA algorithm (HKT-MOEA [39]), bi-objective knowledge transfer for MOEA algorithm (BOKT-MOEA [40]), and the helper objective-based multifactorial evolutionary algorithm (HO-MEA [41]); in total, five algorithms are compared for performance.…”

Section: Experiments 4: Performance Analysis Of the Hpim-ktsmoea Algo...mentioning

confidence: 99%

A Hybrid Preference Interaction Mechanism for Multi-Satellite Imaging Dynamic Mission Planning

Yang,

Hu,

Huang

et al. 2024

Electronics

View full text Add to dashboard Cite

The existing multi-satellite dynamic mission planning system hardly satisfies the requirements of fast response time and high mission benefit in highly dynamic situations. In the meantime, a reasonable decision-maker preference mechanism is an additional challenge for multi-satellite imaging dynamic mission planning based on user preferences (MSDMPUP). Therefore, this study proposes the hybrid preference interaction mechanism and knowledge transfer strategy for the multi-objective evolutionary algorithm (HPIM–KTSMOEA). Firstly, an MSDMPUP model based on a task rolling window is constructed to achieve timely updating of the target task importance degree through the simultaneous application of periodic triggering and event triggering methods. Secondly, the hybrid preference interaction mechanism is constructed to plan according to the satellite controller’s preference-based commands in different phases of the optimal search of the mission planning scheme to effectively respond to the dynamic changes in the environment. Finally, a knowledge transfer strategy for the multi-objective evolutionary algorithm is proposed to accelerate population convergence in new environments based on knowledge transfer according to environmental variability. Simulation experiments verify the effectiveness and stability of the method in processing MSDMPUP. This study found that the HPIM–KTSMOEA algorithm has high task benefit, short response time, and high task completion when processing MSDMPUP.

show abstract

Section: Experiments 4: Performance Analysis Of the Hpim-ktsmoea Algo...mentioning

confidence: 99%

A Hybrid Preference Interaction Mechanism for Multi-Satellite Imaging Dynamic Mission Planning

Yang,

Hu,

Huang

et al. 2024

Electronics

View full text Add to dashboard Cite

show abstract

“…Transfer learning 37–40 is the process that uses the knowledge learned from the source domain to deal with the problems in the target domain. It usually transforms and adjusts the parameters of the network model in the source domain and applies it to the target domain 41–45 …”

Section: Base Model Transfermentioning

confidence: 99%

Multihorizons transfer strategy for continuous online prediction of time‐series data in complex systems

Zhou

Wang

2022

Int J of Intelligent Sys

View full text Add to dashboard Cite

The sustainability online prediction is of great significance for higher horizon time-series prediction in the future, and it embodies higher application value in equipment fault prediction and health management. However, compared with one-step time-series prediction, continuous online prediction faces many uncertainties, including error accumulation and lack of information. To realize continuous online prediction of time-series data in complex systems, this paper proposes a continuous online prediction strategy based on multihorizons transfer (OnMultiHorTS), which is used for continuous online prediction tasks of timeseries data. The algorithm aims to use source domain data to provide more effective information for target prediction tasks. However, the time-varying characteristics of time-series data often lead to large differences in data distribution over a long time span, which is difficult to guarantee the assumption that the data are the same distribution. How to construct more effective source domain information based on historical data and existing data, and apply it to the target domain prediction tasks, is one of the focuses of our OnMultiHorTS algorithm. In addition, different from the typical iterative and multistep advance prediction methods, the proposed algorithm regards different prediction tasks as different horizons, which are

show abstract

“…In our future work, the CDLS strategy will be further studied for solving the problems with a highdimensional decision variable space, namely large-scale optimization problems [61]- [62],. Moreover, the performance of particle swarm optimizer combining some transfer learning methods for solving some multi/many-tasking problems [63]- [64] will be also considered in our future work.…”

Section: Specificallymentioning

confidence: 99%

A convergence and diversity guided leader selection strategy for many-objective particle swarm optimization

Lin

et al. 2022

Engineering Applications of Artificial Intelligence

View full text Add to dashboard Cite

Evolutionary multi-task optimization with hybrid knowledge transfer strategy

Cited by 26 publications

References 36 publications

A Hybrid Preference Interaction Mechanism for Multi-Satellite Imaging Dynamic Mission Planning

A Hybrid Preference Interaction Mechanism for Multi-Satellite Imaging Dynamic Mission Planning

Multihorizons transfer strategy for continuous online prediction of time‐series data in complex systems

A convergence and diversity guided leader selection strategy for many-objective particle swarm optimization

Contact Info

Product

Resources

About