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Yulong Ye

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2 papers

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EAAI Journal 2024 Journal Article

A localized decomposition evolutionary algorithm for imbalanced multi-objective optimization

Yulong Ye
Qiuzhen Lin
Ka-Chun Wong
Jianqiang Li
Zhong Ming
Carlos A. Coello Coello

Multi-objective evolutionary algorithms based on decomposition (MOEA/Ds) convert a multi-objective optimization problem (MOP) into a set of scalar subproblems, which are then optimized in a collaborative manner. However, when tackling imbalanced MOPs, the performance of most MOEA/Ds will evidently deteriorate, as a few solutions will replace most of the others in the evolutionary process, resulting in a significant loss of diversity. To address this issue, this paper suggests a localized decomposition evolutionary algorithm (LDEA) for imbalanced MOPs. A localized decomposition method is proposed to assign a local region for each subproblem, where the inside solutions are associated and the solution update is restricted inside (i. e. , solutions are only replaced by offspring within the same local region). Once off-spring are generated within an originally empty region, the best one is reserved for this subproblem to extend diversity. Meanwhile, the subproblem with the largest number of associated solutions will be found and one of its associated solutions with the worst aggregated value will be removed. Moreover, to speed up convergence for each subproblem while balancing the population's diversity, LDEA only evolves the best-associated solution in each subproblem and correspondingly tailors two decomposition methods in the environmental selection. When compared to nine competitive MOEAs, LDEA has shown the advantages in tackling two benchmark sets of imbalanced MOPs, one benchmark set of balanced yet complicated MOPs, and one real-world MOP.

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EAAI Journal 2023 Journal Article

A Kriging model-based evolutionary algorithm with support vector machine for dynamic multimodal optimization

Xunfeng Wu
Qiuzhen Lin
Wu Lin
Yulong Ye
Qingling Zhu
Victor C.M. Leung

Dynamic multimodal optimization problems (DMMOPs) have to search multiple global optimal solutions with the objectives and constraints dynamically changing over time. In recent years, dynamic optimization problems and multimodal optimization problems have been extensively studied in the field of evolutionary computation. However, DMMOPs have not yet been paid significant attention and only a few studies have been designed for dynamic multimodal optimization. The key issue in optimizing DMMOPs is to address the challenges induced by both the multimodal nature and the dynamic nature. Existing works perform poorly in locating all global optima in static environments and tracking global optima with various change modes. Therefore, in this paper, a Kriging Model-based Evolutionary Algorithm with Support Vector Machine called KMEA-SVM is proposed for tackling DMMOPs. Two important operators are designed in this algorithm, including a Kriging-based preselection and a support vector machine (SVM)-based prediction. The aim of Kriging-based preselection is to search all global optimal solutions more efficiently by preselecting promising solutions with a trained Kriging model, while the purpose of SVM-based prediction is to predict more outstanding solutions as the initial population for new environment when the environment changes. The proposed KMEA-SVM is compared with several state-of-the-art evolutionary algorithms on twenty-four test DMMOPs and the experimental results validate the advantages of KMEA-SVM on seeking more multiple optima in dynamic environments.

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