A correlation-guided layered prediction approach for evolutionary dynamic multiobjective optimization
When solving dynamic multiobjective optimization problems (DMOPs) by evolutionary
algorithms, the historical moving directions of some special points along the Pareto front …
algorithms, the historical moving directions of some special points along the Pareto front …
A novel dynamic multiobjective optimization algorithm with hierarchical response system
In this article, a novel dynamic multiobjective optimization algorithm (DMOA) is proposed
based on a designed hierarchical response system (HRS). Named HRS-DMOA, the …
based on a designed hierarchical response system (HRS). Named HRS-DMOA, the …
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 …
Knowledge guided Bayesian classification for dynamic multi-objective optimization
Dynamic multi-objective optimization problems (DMOPs) typically contain multiple conflicting
objectives that vary over time, requiring the optimization algorithms to quickly track the …
objectives that vary over time, requiring the optimization algorithms to quickly track the …
A framework based on historical evolution learning for dynamic multiobjective optimization
Dynamic multiobjective optimization problems (DMOPs) are widely encountered in real-
world applications and have received considerable attention in recent years. During the …
world applications and have received considerable attention in recent years. During the …
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 …
Decision space partition based surrogate-assisted evolutionary algorithm for expensive optimization
Y Liu, J Liu, S Tan - Expert Systems with Applications, 2023 - Elsevier
In expensive optimization, function evaluations are based on expensive physical
experiments or time consuming simulations. Moreover, the gradient for the objective is not …
experiments or time consuming simulations. Moreover, the gradient for the objective is not …
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 …
Load optimization scheduling of chip mounter based on hybrid adaptive optimization algorithm
A chip mounter is the core equipment in the production line of the surface-mount technology,
which is responsible for finishing the mount operation. It is the most complex and time …
which is responsible for finishing the mount operation. It is the most complex and time …
Solving Expensive Optimization Problems in Dynamic Environments with Meta-learning
Dynamic environments pose great challenges for expensive optimization problems, as the
objective functions of these problems change over time and thus require remarkable …
objective functions of these problems change over time and thus require remarkable …