Dynamic Multi-Objective Evolutionary Algorithm Based on Spectral Clustering Prediction

To address the challenge of rapidly tracking the new pareto optimal set (PS) after environmental changes in dynamic multi-objective optimization problems (DMOPs), this paper proposes a dynamic multi-objective evolutionary algorithm (DMOEA) based on spectral clustering prediction (SCP). The algorithm...

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Vydané v:	IEEE access Ročník 13; s. 176421 - 176433
Hlavní autori:	Sheng, Zhao-Jun, Li, Erchao
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Centroids Clustering Clustering algorithms Concavity Convexity Dynamic multi-objective optimization Dynamic tests evolutionary algorithm Evolutionary algorithms Evolutionary computation Genetic algorithms Heuristic algorithms Multiple objective analysis Optimization Pareto optimization Pareto optimum prediction Prediction algorithms Predictive models Recurrent neural networks Shape spectral clustering Transfer learning
ISSN:	2169-3536, 2169-3536
On-line prístup:	Získať plný text
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