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Cooperative Multi-fitness Evolutionary Algorithm for Scientific Workflows Scheduling

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Bioinspired Systems for Translational Applications: From Robotics to Social Engineering (IWINAC 2024)

Abstract

Scheduling problems require evolutionary methods, but they often struggle with complexity. To enhance solutions, heuristic knowledge can be integrated into fitness functions, although this may introduce bias towards local minima. This paper proposes a cooperative multi-fitness approach that combines genetic diversity with heuristic solutions to support a standard fitness function. Lamarckism can assist in the reconstruction of chromosomes, for direct evaluation by the standard fitness decoder. This combination of genetic diversity and heuristic knowledge aims to achieve superior solutions. This evaluation approach is applied to a genetic algorithm for scientific workflow scheduling, minimizing total execution time in cloud computing.

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References

  1. Barredo, P., Puente, J.: Precise makespan optimization via hybrid genetic algorithm for scientific workflow scheduling problem. Nat. Comput. 22, 615–630 (2023)

    Article  MathSciNet  Google Scholar 

  2. Coleman, T., Casanova, H., Pottier, L., Kaushik, M., Deelman, E., Ferreira da Silva, R.: WfCommons: a framework for enabling scientific workflow research and development. Future Gener. Comput. Syst. 128, 16–27 (2022)

    Google Scholar 

  3. Houck, C.R., Joines, J.A., Kay, M.G.: Utilizing Lamarckian evolution and the Baldwin effect in hybrid genetic algorithms. North Carolina State Univ., Department of Industrial Engineering (1996)

    Google Scholar 

  4. Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Futur. Gener. Comput. Syst. 29(3), 682–692 (2013)

    Article  Google Scholar 

  5. Madni, S.H.H., Abd Latiff, M.S., Abdullahi, M., Abdulhamid, S.M., Usman, M.J.: Performance comparison of heuristic algorithms for task scheduling in IaaS cloud computing environment. PLoS ONE 12(5), 1–26 (2017)

    Article  Google Scholar 

  6. Nebro, A.J., Pérez-Abad, J., Aldana-Martin, J.F., García-Nieto, J.: Evolving a multi-objective optimization framework. Appl. Optim. Swarm Intell., 175–198 (2021)

    Google Scholar 

  7. Topcuoglu, H., Hariri, S., Wu, M.Y.: Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE Trans. Parallel Distrib. Syst. 13(3), 260–274 (2002)

    Article  Google Scholar 

  8. Wu, C., et al.: Genetic Algorithm with Multiple Fitness Functions for Generating Adversarial Examples.: IEEE Congress on Evolutionary Computation, CEC 2021 - Proceedings, 1792–1799, p. 2021. Kraków, Poland (2021)

    Google Scholar 

  9. Yates, C., Christopher, R., Tumer, K.: Multi-fitness learning for behavior-driven cooperation. In: GECCO 2020 - Proceedings of the 2020 Genetic and Evolutionary Computation Conference, pp. 453–461. Cancun. (2020)

    Google Scholar 

  10. Ye, X., Li, J., Liu, S., et al.: A hybrid instance-intensive workflow scheduling method in private cloud environment. Nat. Comput. 18, 735–746 (2019)

    Article  MathSciNet  Google Scholar 

  11. Zhu, Z., Zhang, G., Li, M., Liu, X.: Evolutionary multi-objective workflow scheduling in cloud. Trans. Parallel Distributed Syst. 27(5), 1344–1357 (2016)

    Article  Google Scholar 

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Acknowledgements

This research has been supported by the Spanish Government under research grants PID2022-141746OB-I00 and TED2021-131938B-I00.

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Correspondence to Jorge Puente .

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Barredo, P., Puente, J. (2024). Cooperative Multi-fitness Evolutionary Algorithm for Scientific Workflows Scheduling. In: Ferrández Vicente, J.M., Val Calvo, M., Adeli, H. (eds) Bioinspired Systems for Translational Applications: From Robotics to Social Engineering. IWINAC 2024. Lecture Notes in Computer Science, vol 14675. Springer, Cham. https://doi.org/10.1007/978-3-031-61137-7_17

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  • DOI: https://doi.org/10.1007/978-3-031-61137-7_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-61136-0

  • Online ISBN: 978-3-031-61137-7

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