Abstract
In the realm of optimization, where intricate landscapes conceal possibly hidden pathways to high-quality solutions, STNWeb serves as a beacon of clarity. This novel web-based visualization platform empowers researchers to delve into the intricate interplay between algorithms and optimization problems, uncovering the factors that influence algorithm performance across diverse problem domains, be they discrete/combinatorial or continuous. By leveraging the inherent power of visual data representation, STNWeb transcends traditional analytical methods, providing a robust foundation for dissecting algorithm behavior and pinpointing the mechanisms that elevate one algorithm above another. This visually-driven approach fosters a deeper understanding of algorithmic strengths and weaknesses, ultimately strengthening the discourse surrounding algorithm selection and refinement for complex optimization tasks.
The research presented in this paper was supported by grants TED2021-129319B-I00 and PID2022-136787NB-I00 funded by MCIN/AEI/10.13039/501100011033.
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Notes
- 1.
In practice, the HAC approach generally results in more informative STN plots than the standard strategy, but it also tends to require more computational time.
References
Chacón Sartori, C., Blum, C., Ochoa, G.: STNWeb: a new visualization tool for analyzing optimization algorithms. Softw. Impacts 17, 100558 (2023). https://doi.org/10.1016/j.simpa.2023.100558. https://www.sciencedirect.com/science/article/pii/S2665963823000957
Lavinas, Y., Aranha, C., Ochoa, G.: Search trajectories networks of multiobjective evolutionary algorithms. In: Jiménez Laredo, J.L., Hidalgo, J.I., Babaagba, K.O. (eds.) EvoApplications 2022. LNCS, vol. 13224, pp. 223–238. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-02462-7_15
Ochoa, G., Liefooghe, A., Lavinas, Y., Aranha, C.: Decision/objective space trajectory networks for multi-objective combinatorial optimisation. In: Pérez Cáceres, L., Stützle, T. (eds.) EvoCOP 2023. LNCS, vol. 13987, pp. 211–226. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-30035-6_14
Ochoa, G., Malan, K.M., Blum, C.: Search trajectory networks: a tool for analysing and visualising the behaviour of metaheuristics. Appl. Soft Comput. 109, 107492 (2021). https://doi.org/10.1016/j.asoc.2021.107492. https://www.sciencedirect.com/science/article/pii/S1568494621004154
OpenAI, et al.: GPT-4 technical report (2023). https://arxiv.org/abs/2303.08774
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Chacón Sartori, C., Blum, C. (2024). STNWeb for the Analysis of Optimization Algorithms: A Short Introduction. In: Sevaux, M., Olteanu, AL., Pardo, E.G., Sifaleras, A., Makboul, S. (eds) Metaheuristics. MIC 2024. Lecture Notes in Computer Science, vol 14754. Springer, Cham. https://doi.org/10.1007/978-3-031-62922-8_29
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DOI: https://doi.org/10.1007/978-3-031-62922-8_29
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