A prediction strategy based on special points and multiregion knee points for evolutionary dynamic multiobjective optimization

Wei, Lixin; Guo, Zeyin; Fan, Rui; Sun, Hao; Zhao, Zikai

doi:10.1007/s10489-020-01772-7

Cited by 6 publications

(2 citation statements)

References 39 publications

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“…The special point set that eliminates useless individuals can make the predicted population track POF more accurately. Knee points were also adopted in [29] and [30] to facilitate the tracking ability. Wu et al [31] introduced a directed search strategy to predict a new population, in which the moving direction of POS is estimated by the position of centroid points.…”

Section: B Related Workmentioning

confidence: 99%

An Online Prediction Approach Based on Incremental Support Vector Machine for Dynamic Multiobjective Optimization

Xu¹,

Jiang²,

Hu³

et al. 2021

Preprint

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Real-world multiobjective optimization problems usually involve conflicting objectives that change over time, which requires the optimization algorithms to quickly track the Pareto optimal front (POF) when the environment changes. In recent years, evolutionary algorithms based on prediction models have been considered promising. However, most existing approaches only make predictions based on the linear correlation between a finite number of optimal solutions in two or three previous environments. These incomplete information extraction strategies may lead to low prediction accuracy in some instances. In this paper, a novel prediction algorithm based on incremental support vector machine (ISVM) is proposed, called ISVM-DMOEA. We treat the solving of dynamic multiobjective optimization problems (DMOPs) as an online learning process, using the continuously obtained optimal solution to update an incremental support vector machine without discarding the solution information at earlier time. ISVM is then used to filter random solutions and generate an initial population for the next moment. To overcome the obstacle of insufficient training samples, a synthetic minority oversampling strategy is implemented before the training of ISVM. The advantage of this approach is that the nonlinear correlation between solutions can be explored online by ISVM, and the information contained in all historical optimal solutions can be exploited to a greater extent. The experimental results and comparison with chosen state-of-the-art algorithms demonstrate that the proposed algorithm can effectively tackle dynamic multiobjective optimization problems.

show abstract

Section: B Related Workmentioning

confidence: 99%

An Online Prediction Approach Based on Incremental Support Vector Machine for Dynamic Multiobjective Optimization

Xu¹,

Jiang²,

Hu³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…In the literature, most existing DMOEAs are designed based on the two assumptions mentioned above [12], [17], [18], [19]. For example, Feng et al proposed solving DMOPs via autoencoding evolutionary search [20].…”

mentioning

confidence: 99%

A Mahalanobis Distance-Based Approach for Dynamic Multiobjective Optimization With Stochastic Changes

Zheng

Jiang

et al. 2024

IEEE Trans. Evol. Computat.

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Efficient bi-level multi objective approach for budget-constrained dynamic Bag-of-Tasks scheduling problem in heterogeneous multi-cloud environment

et al. 2022

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A prediction strategy based on special points and multiregion knee points for evolutionary dynamic multiobjective optimization

Cited by 6 publications

References 39 publications

An Online Prediction Approach Based on Incremental Support Vector Machine for Dynamic Multiobjective Optimization

An Online Prediction Approach Based on Incremental Support Vector Machine for Dynamic Multiobjective Optimization

A Mahalanobis Distance-Based Approach for Dynamic Multiobjective Optimization With Stochastic Changes

Efficient bi-level multi objective approach for budget-constrained dynamic Bag-of-Tasks scheduling problem in heterogeneous multi-cloud environment

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