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Anqi Pan

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3 papers

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EAAI Journal 2025 Journal Article

A bilevel coevolution framework with knowledge transfer for large-scale optimization and its application in multiperiod economic dispatch

Anqi Pan
Haifeng Liu
Yinghao Shan
Bo Shen

Complex systems typically consist of multiple components and serve requirements across multiple periods. Their optimization involves large-scale parameters. If all parameters are considered at one time, the high-dimensional searching space will present great challenge. Otherwise, if parameters are considered partially, the global fittest solutions can hardly be found. Keep these in mind, in this paper, a novel bilevel coevolution framework with knowledge transfer (BiKT) is introduced for large-scale multiobjective optimization. Specifically, in this framework, the optimization problem is decomposed to several low-dimensional subproblems, establishing a bilevel structure. Then, the original problem and the subproblems are regarded as two tasks, their coevolution is realized by converting searching agents between the upper and lower optimization workflows, fulfilling exploration in global optimization and exploitation in local areas. Meanwhile, a knowledge transfer strategy is studied to adapt the search directions and accelerate convergence speeds. The superiority of the novel framework has been verified by experimental studies on large-scale benchmark problems, and the effectiveness of knowledge transfer has been discussed through ablation experiments. In the end, the proposed method is employed to tackle a large-scale real-world challenge, the multiperiod economic dispatch problem in the power system. After problem formulation and analysis, the proposed method can perfectly solve the application.

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EAAI Journal 2025 Journal Article

A new adaptive robust multi-objective optimization algorithm for dispatching of microgrids design

Xue Feng
Lei Cui
Dexin Ren
Anqi Pan
Juchen Hong

Uncertainty widely exists in real-world applications. When uncertainty occurs, the robustness of solutions obtained by optimization is critical to the operation of the application. In the process of solving robust solutions, the balance between robustness and convergence is the key problem. Attaching undue importance to robustness leads to local optimality, while excessive convergence leads to loss of robust solutions. Motivated by above, a new adaptive robust multi-objective optimization algorithm is proposed. This algorithm proposes both a convergence-driven strategy and a robustness-driven strategy, and it adaptively selects between them by evaluating the population's evolutionary state. In the robust driven strategy, a new robustness metric is proposed to evaluate the robustness of individuals under disturbance. A penalty function based on the proposed metric guides the environmental selection process to enhance robustness. In the convergent driven strategy, the environment selection is mainly driven by the non-dominant relationship. A region robustness estimation strategy is proposed to evaluate the individuals in critical layer. Compared to four state of-the-art multi-objective algorithms on benchmark suits, the proposed algorithm performs better than its peers. In addition, the robust optimization problem of the dispatching of microgrids is constructed and the proposed algorithm is used for optimization of microgrids. The effectiveness of experimental results reveal that our methods are very promising in tackling the dispatching of microgrids problems.

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EAAI Journal 2024 Journal Article

Radial projection-based adaptive sampling strategies for surrogate-assisted many-objective optimization

Juchen Hong
Anqi Pan
Zhengyun Ren
Xue Feng

Intelligent manufacturing and industrial control systems frequently encounter expensive many-objective optimization problems (EMaOPs). Surrogate-assisted evolutionary algorithms (SAEAs) build predictive models to substitute the expensive fitness evaluation, enabling them to solve optimization more efficiently. In SAEAs, to enhance the exploration of optimization and the generalization of the surrogate model, the diversity of infill offspring and training database should be well-maintained, which is challenging in high-dimensional spaces or problems with disconnected Pareto front. This paper suggests a radial projection-based surrogate-assisted framework for solving EMaOPs. The radial projection can map the high-dimensional objective space into a 2-dimensional radial space. Based on this, a dynamic quadratic division method is proposed to enhance the diversity of solutions. Furthermore, an adaptive infill sampling criterion is introduced based on the distribution of selected convergent solutions, and a training database updating strategy is designed under the premise of maintaining its diversity and the model training efficiency. The presented framework exhibits a notable level of flexibility and adaptability as it can be effortlessly combined with other multi-objective optimization algorithms. Several experimental results on a set of expensive multi/many-objective test problems have demonstrated the superiority of the framework.

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