Benchmarking Continuous Dynamic Multi-Objective Optimization: Survey and Generalized Test Suite
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866909980995616768 |
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| author | Shao, Chang Zhao, Qi Pu, Nana Cheng, Shi Jiang, Jing Shi, Yuhui |
| author_facet | Shao, Chang Zhao, Qi Pu, Nana Cheng, Shi Jiang, Jing Shi, Yuhui |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_01317 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Benchmarking Continuous Dynamic Multi-Objective Optimization: Survey and Generalized Test Suite Shao, Chang Zhao, Qi Pu, Nana Cheng, Shi Jiang, Jing Shi, Yuhui Neural and Evolutionary Computing 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. |
| title | Benchmarking Continuous Dynamic Multi-Objective Optimization: Survey and Generalized Test Suite |
| topic | Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2601.01317 |