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Wenxiang Jiang

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3 papers

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EAAI Journal 2026 Journal Article

A knowledge-based memetic algorithm for integrated scheduling of equipment operation and spare parts manufacturing in distributed assembly flexible job shops

Wenxiang Jiang
Qianwang Deng
Qiang Luo
Jingxing Zhang
Jicong Zhou

Under the development context of Industry 4. 0, researches on the integration of equipment operation and maintenance (O&M) activities with spare parts manufacturing have garnered increasing attention. Meanwhile, new challenges emerge in spare parts manufacturing due to the increasing complexity of equipment. However, existing integrated scheduling studies have been simplified in the spare parts manufacturing, making it difficult to cope with actual spare parts replacement scenarios of complex equipment. To address this gap, this paper investigates an integrated scheduling problem (ISP-PAO) that includes distributed flexible production, flexible assembly, and operational strategies of complex equipment. We formalize the ISP-PAO through a mathematical model with dual objectives of minimizing total energy consumption and maximizing operational utility. Furthermore, several problem-specific knowledge properties are systematically analyzed and proved, and a knowledge-based memetic algorithm (KBMA) is further proposed to solve the problem. To strengthen optimization capability, the algorithm incorporates four initialization strategies, five knowledge-based local search operators and an energy-aware pareto front refinement strategy. Extensive experiments validate the effectiveness of proposed components, and comparative studies comprehensively evaluate the superiority and robustness of the KBMA, demonstrating its exceptional performance in addressing the ISP-PAO.

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EAAI Journal 2025 Journal Article

Evolutionary dynamic multiobjective optimization using a Bayesian vector autoregression prediction model

Kai Gao
Wenxiang Jiang
Lihong Xu

Dynamic multiobjective optimization problems (DMOPs), inherently involving the simultaneous optimization of conflicting objectives under time-varying environments, exhibit ubiquitous presence in real-world applications spanning diverse engineering disciplines. A prevalent limitation in existing dynamic multiobjective optimization evolutionary algorithms (DMOEAs) lies in inadequate utilization of historical information and neglect of interdependencies among decision variables, which frequently induces suboptimal initial population predictions deviating from true Pareto optimal sets. To mitigate these limitations, we propose MOEA/D-BVAR, a novel DMOEA framework incorporating a Bayesian vector autoregressive (BVAR) model that conceptualizes solution dynamics through holistic vector forecasting rather than isolated variable-specific prediction. The algorithm initially clusters variables via mutual information correlation analysis, subsequently constructing BVAR models for each cluster to project their evolutionary trajectories. Independently varying variables are rapidly predicted through differential forecasting models. A multivariate interaction optimization mechanism enhances search efficiency. Comprehensive empirical evaluations on 14 benchmark suites compare MOEA/D-BVAR against six state-of-the-art DMOEAs developed over the past five years. Statistical analysis of experimental outcomes demonstrates the proposed algorithm’s superior competitiveness in handling complex DMOPs.

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AAAI Conference 2024 Conference Paper

NeRFail: Neural Radiance Fields-Based Multiview Adversarial Attack

Wenxiang Jiang
Hanwei Zhang
Xi Wang
Zhongwen Guo
Hao Wang

Adversarial attacks, i.e., generating adversarial perturbations with a small magnitude to deceive deep neural networks, are important for investigating and improving model trustworthiness. Traditionally, the topic was scoped within 2D images without considering 3D multiview information. Benefiting from Neural Radiance Fields (NeRF), one can easily reconstruct a 3D scene with a Multi-Layer Perceptron (MLP) from given 2D views and synthesize photo-realistic renderings of novel vantages. This opens up a door to discussing the possibility of undertaking to attack multiview NeRF network with downstream tasks from different rendering angles, which we denote Neural Radiance Fiels-based multiview adversarial Attack (NeRFail). The goal is, given one scene and a subset of views, to deceive the recognition results of agnostic view angles as well as given views. To do so, we propose a transformation mapping from pixels to 3D points such that our attack generates multiview adversarial perturbations by attacking a subset of images with different views, intending to prevent the downstream classifier from correctly predicting images rendered by NeRF from other views. Experiments show that our multiview adversarial perturbations successfully obfuscate the downstream classifier at both known and unknown views. Notably, when retraining another NeRF on the perturbed training data, we show that the perturbation can be inherited and reproduced. The code can be found at https://github.com/jiang-wenxiang/NeRFail.

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