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Parallel algorithm portfolios with adaptive resource allocation strategy

Abstract

Algorithm portfolios are multi-algorithmic schemes that combine a number of solvers into a joint framework for solving global optimization problems. A crucial part of such schemes is the resource allocation process that is responsible for assigning computational resources to the constituent algorithms. We propose a resource allocation process based on adaptive decision-making procedures. The proposed approach is incorporated in algorithm portfolios composed of three essential types of numerical optimization algorithms, namely gradient-based, direct search, and swarm intelligence algorithms. The designed algorithm portfolios are experimentally demonstrated on a challenging optimization problem for different dimensions and experimental settings. The accompanying statistical analysis offers interesting conclusions and insights on the performance of the algorithm portfolio compared to its constituent algorithms, as well as on the effect of its parameters.

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Data availability statement

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

References

  1. Almakhlafi, A., Knowles, J.: Systematic construction of algorithm portfolios for a maintenance scheduling problem. In: IEEE Congress On Evolutionary Computation, pp. 245–252. Cancun, Mexico (2013)

  2. Battiti, R., Mascia, F.: An algorithm portfolio for the sub-graph isomorphism problem. In: Stützle, T., Birattari, M., Hoos, H.H. (eds.) Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics, International Workshop, SLS. Lecture Notes in Computer Science, vol. 4638, pp. 106–120. Springer, New York (2007)

    Chapter  Google Scholar 

  3. Calderín, J.F., Masegosa, A.D., Pelta, D.A.: An algorithm portfolio for the dynamic maximal covering location problem. Memet. Comput. 9, 141–151 (2016)

    Article  Google Scholar 

  4. Clerc, M., Kennedy, J.: The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans. Evol. Comput. 6(1), 58–73 (2002)

    Article  Google Scholar 

  5. Goldberg, D.E.: Probability matching, the magnitude of reinforcement, and classifier system bidding. Mach. Learn. 5, 407–425 (1990)

    Google Scholar 

  6. Huberman, B.A., Lukose, R.M., Hogg, T.: An economics approach to hard computational problems. Science 27, 51–53 (1997)

    Article  Google Scholar 

  7. Loreggia, A., Malitsky, Y., Samulowitz, H., Saraswat, V.: Deep learning for algorithm portfolios. In: Thirtieth AAAI Conference on Artificial Intelligence, pp. 1280–1286. Phoenix, Arizona(2016)

  8. Müller, C.L., Sbalzarini, I.F.: Energy landscapes of atomic clusters as black box optimization benchmarks. Evol. Comput. 20(4), 543–573 (2012)

    Article  Google Scholar 

  9. Nelder, J.A., Mead, R.: A simplex method for function minimization. Comput. J. 7(4), 308–313 (1965)

    MathSciNet  Article  Google Scholar 

  10. Nocedal, J., Wright, S.J.: Numerical Optimization. Springer, New York (2006)

    MATH  Google Scholar 

  11. Pardalos, P.M., Shalloway, D., Xue, G. (eds.): Global Minimization of Nonconvex Energy Functions: Molecular Conformation and Protein Folding, DIMACS-Series in Discrete Mathematics and Theoretical Computer Science, vol. 23. AMS, Providence (1996)

    Google Scholar 

  12. Parsopoulos, K.E., Vrahatis, M.N.: Particle Swarm Optimization and Intelligence: Advances and Applications. Information Science Publishing (IGI Global), New York (2010)

    Book  Google Scholar 

  13. Peng, F., Tang, K., Chen, G., Yao, X.: Population-based algorithm portfolios for numerical optimization. IEEE Trans. Evol. Comput. 14(5), 782–800 (2010)

    Article  Google Scholar 

  14. Rice, J.R.: The algorithm selection problem. Adv. Comput. 15, 65–118 (1976)

    Article  Google Scholar 

  15. Shukla, N., Dashora, Y., Tiwari, M., Chan, F., Wong, T.: Introducing algorithm portfolios to a class of vehicle routing and scheduling problem. In: Operations and Supply Chain Management (OSCM 2007), pp. 1015–1026. Bangkok, Thailand (2007)

  16. Souravlias, D., Parsopoulos, K.E., Alba, E.: Parallel algorithm portfolio with market trading-based time allocation. In: Lübbecke, M., et al. (eds.) Operations Research Proceedings 2014, pp. 567–574. Springer, New York (2016)

    Chapter  Google Scholar 

  17. Souravlias, D., Parsopoulos, K.E., Kotsireas, I.S.: Circulant weighing matrices: a demanding challenge for parallel optimization metaheuristics. Optim. Lett. 10(6), 1303–1314 (2016)

    MathSciNet  Article  Google Scholar 

  18. Souravlias, D., Parsopoulos, K.E., Meletiou, G.C.: Designing bijective S-boxes using algorithm portfolios with limited time budgets. Appl. Soft Comput. 59, 475–486 (2017)

    Article  Google Scholar 

  19. Souravlias, D., Parsopoulos, K.E., Kotsireas, I.S., Pardalos, P.M.: Algorithm Portfolios: Advances, Applications, and Challenges. Springer Briefs in Optimization. Springer, New York (2021)

    Book  Google Scholar 

  20. Tang, K., Peng, F., Chen, G., Yao, X.: Population-based algorithm portfolios with automated constituent algorithms selection. Inf. Sci. 279, 94–104 (2014)

    Article  Google Scholar 

  21. Thathachar, M.A.L., Sastry, P.S.: A class of rapidly converging algorithms for learning automata. IEEE Trans. Syst. Man Cybern. 15, 168–175 (1985)

    Article  Google Scholar 

  22. Thierens, D.: An adaptive pursuit strategy for allocating operator probabilities. In: Proceedings of the 7th Annual Conference on Genetic and Evolutionary Computation (GECCO’05), pp. 1539–1546. ACM (2005)

  23. Wawrzyniak, J., Drozdowski, M., Sanlaville, É.: Selecting algorithms for large berth allocation problems. Eur. J. Oper. Res. 283(3), 844–862 (2020)

    MathSciNet  Article  Google Scholar 

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Acknowledgements

This research is co-financed by Greece and the European Union (European Social Fund-ESF) through the Operational Programme “Human Resources Development, Education and Lifelong Learning” in the context of the project “Reinforcement of Postdoctoral Researchers-2nd Cycle” (MIS-5033021), implemented by the State Scholarships Foundation (IKY).

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Correspondence to Konstantinos E. Parsopoulos.

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Parsopoulos, K.E., Tatsis, V.A., Kotsireas, I.S. et al. Parallel algorithm portfolios with adaptive resource allocation strategy. J Glob Optim (2022). https://doi.org/10.1007/s10898-022-01162-y

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  • DOI: https://doi.org/10.1007/s10898-022-01162-y

Keywords

  • Algorithm portfolios
  • Adaptive pursuit
  • Resource allocation
  • Global optimization
  • Metaheuristics

Mathematics Subject Classification

  • 68T20
  • 68W20
  • 90C26