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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Bibliographic Details
Published in:IEEE access Vol. 13; pp. 176421 - 176433
Main Authors: Sheng, Zhao-Jun, Li, Erchao
Format: Journal Article
Language:English
Published: Piscataway IEEE 2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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
Online Access:Get full text
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Summary: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 first applies spectral clustering to the PS from historical moments to automatically identify multiple subclasses of the PS. When environmental changes occur, the algorithm uses historical information to predict the centroids and shapes of each subclass, thereby constructing a new initial population. Experimental results on 14 standard dynamic test problems indicate that the proposed SCP-DMOEA outperforms four typical algorithms-PPS, MDP, KTM, and RNN-in handling various change characteristics in most test functions. These characteristics include translation or rotation changes of the PS, correlations between decision variables, and time-varying mixed pareto optimal fronts (PFs), including variations in convexity and concavity. In particular, SCP-DMOEA also shows superior performance when handling optimization problems with more complex PF changes, such as the position of the PF continuously changes over time, the objective vector oscillates among several modes, and the PFs are discontinuous.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2025.3617239