A novel dynamic multiobjective optimization algorithm with non-inductive transfer learning based on multi-strategy adaptive selection
In this article, a novel multi-strategy adaptive selection-based dynamic multiobjective
optimization algorithm (MSAS-DMOA) is proposed, which adopts the non-inductive transfer …
optimization algorithm (MSAS-DMOA) is proposed, which adopts the non-inductive transfer …
Large language model for multi-objective evolutionary optimization
Multiobjective evolutionary algorithms (MOEAs) are major methods for solving multiobjective
optimization problems (MOPs). Many MOEAs have been proposed in the past decades, of …
optimization problems (MOPs). Many MOEAs have been proposed in the past decades, of …
Multi-source online transfer learning based on hybrid physics-data model for cross-condition tool health monitoring
B Qiang, K Shi, J Ren, Y Shi - Journal of Manufacturing Systems, 2024 - Elsevier
Prognostic maintenance (PM) aims to monitor the running status and promptly detect
potential failures to improve the availability and productivity of the equipment. The …
potential failures to improve the availability and productivity of the equipment. The …
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 …
Elitism-based transfer learning and diversity maintenance for dynamic multi-objective optimization
In handling dynamic multi-objective optimization problems (DMOPs), transfer learning driven
methods have received considerable attention for finding a high-quality initial population …
methods have received considerable attention for finding a high-quality initial population …
Dynamic multiobjective evolutionary optimization via knowledge transfer and maintenance
This article suggests a new dynamic multiobjective evolutionary algorithm (DMOEA) with
Knowledge Transfer and Maintenance, called KTM-DMOEA, which aims to alleviate the …
Knowledge Transfer and Maintenance, called KTM-DMOEA, which aims to alleviate the …
Addressing modern and practical challenges in machine learning: A survey of online federated and transfer learning
Online federated learning (OFL) and online transfer learning (OTL) are two collaborative
paradigms for overcoming modern machine learning challenges such as data silos …
paradigms for overcoming modern machine learning challenges such as data silos …
Multi-condition operational optimization with adaptive knowledge transfer for municipal solid waste incineration process
Y Cui, X Meng, J Qiao - Expert Systems with Applications, 2024 - Elsevier
To satisfy incineration efficiency and nitrogen oxides ultra-low emission requirements, it is of
great importance to improve the operational performance of the municipal solid waste …
great importance to improve the operational performance of the municipal solid waste …
A cluster prediction strategy with the induced mutation for dynamic multi-objective optimization
Dynamic multi-objective optimization problems (DMOPs) are multi-objective optimization
problems in which at least one objective and/or related parameter vary over time. The …
problems in which at least one objective and/or related parameter vary over time. The …
The IGD-based prediction strategy for dynamic multi-objective optimization
In recent years, an increasing number of prediction-based strategies have shown promising
results in handling dynamic multi-objective optimization problems (DMOPs), and prediction …
results in handling dynamic multi-objective optimization problems (DMOPs), and prediction …