.png){kind=icon}
Return
A weighted knowledge extraction strategy for dynamic multi-objective optimization
.png){kind=icon}
PRE
AI
DOI：10.1016/j.swevo.2024.101773.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Multi-objective evolutionary algorithms suffer from performance degradation when solving dynamic multi- objective optimization problems (DMOPs) with a new conditional configuration from scratch, which motivates the research on knowledge extraction. However, most knowledge extraction strategies only focus on obtaining effective information from a single knowledge source, while ignoring the useful information from other knowledge sources with similar properties. Motivated by this, a weighted multi-source knowledge extraction strategy-based dynamic multiobjective evolutionary algorithm is proposed. First, a similarity criterion based on angle information is constructed to quantify similarity between different source domains and the target domain. Second, a knowledge extraction technique is developed to select a specific number of individuals from each source domain using a distance metric. Third, a generation strategy based on dynamic weighting mechanism is proposed, which generates a certain number of individuals and merges these individuals into the initial population within the new environment. Finally, the comprehensive experiments are conducted on public DMOP benchmarks and demonstrate the devised method significantly outperforms the state-of-the-art competing algorithms.
Keywords:
Dynamic multiobjective optimization
Change response
Evolutionary algorithms
Evolutionary environment
Journal
IF:
8.5
Papers:
2.2K
Citations:
1.0W
Organization
Cited Papers
Solving dynamic multi-objective problems with an evolutionary multi-directional search approach
PREAI
ShareSaveShareSaveA prediction strategy based on decision variable analysis for dynamic Multi-objective Optimization
OAAI
OAAIShareSaveA directed search strategy for evolutionary dynamic multiobjective optimization.png)
SOFT COMPUTING
SOFT COMPUTINGIF2.5
PREAI
ShareSaveA General Framework of Multipopulation Methods with Clustering in Undetectable Dynamic Environments
OAAI
OAAIShareSaveShareSaveA Co-evolutionary Multi-population Evolutionary Algorithm for Dynamic Multiobjective Optimization
PREAI
ShareSave
View more