preprint · arXiv (Cornell University) · 2026

Benchmarking Continuous Dynamic Multi-Objective Optimization: Survey and Generalized Test Suite

Shao Chang, Qi Zhao, Nana Pu, Shi Cheng, Jing Jiang, Yuhui Shi

Cited by 0Open access

Abstract

Dynamic multi-objective optimization (DMOO) has recently attracted increasing interest from both academic researchers and engineering practitioners, as numerous real-world applications that evolve over time can be naturally formulated as dynamic multi-objective optimization problems (DMOPs). This growing trend necessitates advanced benchmarks for the rigorous evaluation of optimization algorithms under realistic conditions. This paper introduces a comprehensive and principled framework for constructing highly realistic and challenging DMOO benchmarks. The proposed framework features several novel components: a generalized formulation that allows the Pareto-optimal Set (PS) to change on hypersurfaces, a mechanism for creating controlled variable contribution imbalances to generate heterogeneous landscapes, and dynamic rotation matrices for inducing time-varying variable interactions and non-separability. Furthermore, we incorporate a temporal perturbation mechanism to simulate irregular environmental changes and propose a generalized time-linkage mechanism that systematically embeds historical solution quality into future problems, thereby capturing critical real-world phenomena such as error accumulation and time-deception. Extensive experimental results validate the effectiveness of the proposed framework, demonstrating its superiority over conventional benchmarks in terms of realism, complexity, and its capability for discriminating state-of-the-art algorithmic performance. This work establishes a new standard for dynamic multi-objective optimization benchmarking, providing a powerful tool for the development and evaluation of next-generation algorithms capable of addressing the complexities of real-world dynamic systems.

Cite this paper

APA 7

Chang, S., Zhao, Q., Pu, N., Cheng, S., Jiang, J., & Shi, Y. (2026). Benchmarking Continuous Dynamic Multi-Objective Optimization: Survey and Generalized Test Suite. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2601.01317

MLA 9

Chang, Shao, et al. “Benchmarking Continuous Dynamic Multi-Objective Optimization: Survey and Generalized Test Suite.” arXiv (Cornell University), 2026, https://doi.org/10.48550/arxiv.2601.01317.

Chicago

Chang, Shao, Qi Zhao, Nana Pu, Shi Cheng, Jing Jiang, and Yuhui Shi. 2026. “Benchmarking Continuous Dynamic Multi-Objective Optimization: Survey and Generalized Test Suite.” arXiv (Cornell University). https://doi.org/10.48550/arxiv.2601.01317.

Harvard

Chang, S. et al. (2026) ‘Benchmarking Continuous Dynamic Multi-Objective Optimization: Survey and Generalized Test Suite’, arXiv (Cornell University). Available at: https://doi.org/10.48550/arxiv.2601.01317.

BibTeX

@misc{chang2026benchmarking,
  title = {{Benchmarking Continuous Dynamic Multi-Objective Optimization: Survey and Generalized Test Suite}},
  author = {Chang, Shao and Zhao, Qi and Pu, Nana and Cheng, Shi and Jiang, Jing and Shi, Yuhui},
  journal = {arXiv (Cornell University)},
  year = {2026},
  doi = {10.48550/arxiv.2601.01317}
}

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