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- W2811288387 abstract "Various real-world multi-objective optimizationproblems are dynamic, requiring evolutionary algorithms to beable to rapidly track the moving Pareto front of an optimizationproblem once an environmental change occurs. To this end,several methods have been developed to predict the new locationof the moving Pareto set so that the population can be reinitializedaround the predicted location. In this paper, we present a multidirectionalprediction strategy to enhance the performance ofevolutionary algorithms in solving a dynamic multi-objectiveoptimization problem. To more accurately predict the movinglocation of the Pareto set, the population is clustered into anumber of representative groups by a proposed classificationstrategy, where the number of clusters is adapted accordingto the severity of the environmental change. To examine theperformance of the proposed algorithm, the proposed predictionstrategy is compared with four state-of-the-art predictionmethods under the framework of particle swarm optimization aswell as five popular evolutionary algorithms for dynamic multiobjectiveoptimization. Our experimental results demonstratethat the proposed algorithm can effectively tackle dynamic multiobjectiveoptimization problems." @default.
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- W2811288387 date "2018-01-01" @default.
- W2811288387 modified "2023-09-23" @default.
- W2811288387 title "Multi-directional Prediction Approach for Dynamic Multi-objective Optimization Problems" @default.
- W2811288387 hasPublicationYear "2018" @default.
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