Abstract
The pseudo-coloring problem (PsCP) is a combinatorial optimization challenge that involves assigning colors to elements in a way that meets specific criteria, often related to minimizing conflicts or maximizing some form of utility. A variety of metaheuristic algorithms have been developed to solve PsCP efficiently. However, these algorithms sometimes struggle with the quality of solutions, impacting their ability to achieve optimal or near-optimal results reliably. To overcome these issues, this study introduces an adapted conscious neighborhood-based crow search algorithm (CCSA) and a massive variant of CCSA specifically tailored for PsCP. The performance of CCSA and MCCSA are evaluated on real and synthetic images and compared with state-of-the-art optimizers. The results showed that the adapted CCSA and MCCSA outperformed offering an effective strategy for image pseudo-colorization.
M. S. Viana and R. C. Contreras—Contributed equally to the work.
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Acknowledgments
We gratefully acknowledge the grants provided by the Brazilian agencies: “Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES)”; “National Council for Scientific and Technological Development (CNPq)” and “The São Paulo Research Foundation (FAPESP)”, respectively through the processes 303854/2022-7 (CNPq - RCG), 2021/12407-4 (FAPESP - RCG), 2022/05186-4 (FAPESP - RCC), 2019/21464-1 (FAPESP - RCC), 2023/06611-3 (FAPESP - MSV) and Finance Code 001 (CAPES - MSV).
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Simplicio Viana, M. et al. (2024). Massive Conscious Neighborhood-Based Crow Search Algorithm for the Pseudo-Coloring Problem. In: Tan, Y., Shi, Y. (eds) Advances in Swarm Intelligence. ICSI 2024. Lecture Notes in Computer Science, vol 14788. Springer, Singapore. https://doi.org/10.1007/978-981-97-7181-3_15
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