Skip to main content

Advertisement

SpringerLink
Log in

A component-based study of energy consumption for sequential and parallel genetic algorithms

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

Recently, energy efficiency has gained attention from researchers interested in optimizing computing resources. Solving real-world problems using optimization techniques (such as metaheuristics) requires a large number of computing resources and time, consuming an enormous amount of energy. However, only a few and limited research efforts in studying the energy consumption of metaheuristics can be found in the existing literature. In particular, genetic algorithms (GAs) are being used so widely to solve a large range of problems in scientific and real-world problems, but hardly found explained in their internal consumption behavior. In the present article, we analyze the energy consumption behavior of such techniques to offer a useful set of findings to researchers in the mentioned domains. We expand our study to include several algorithms and different problems and target the components of the algorithms so that the results are still more appealing for researchers in arbitrary domains of application. Our experiments on the sequential GAs show the controlling role of the fitness operator on energy consumption and also reveal possible energy hot spots in GAs operations, such as mutation operator. Further, our distributed evaluations besides a statistical analysis of the results demonstrate that the communication scheme could highly affect the energy consumption of the parallel evaluations of the GAs.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Abbasi Z, Jonas M, Banerjee A et al (2013) Evolutionary green computing solutions for distributed cyber physical systems. In: Khan S, Kołodziej J, Li J, Zomaya A (eds) Evolutionary based solutions for green computing studies in computational intelligence. Springer, Berlin, pp 1–28

    Google Scholar 

  2. Abdelhafez A, Alba E (2017) Speed-up of synchronous and asynchronous distributed Genetic Algorithms: a first common approach on multiprocessors. In: 2017 IEEE Congress on Evolutionary Computation (CEC)

  3. Alba E, Troya JM (2001) Analyzing synchronous and asynchronous parallel distributed genetic algorithms. Future Gener Comput Syst 17:451–465

    Article  Google Scholar 

  4. Alba E (2005) Parallel metaheuristics: a new class of algorithms. Wiley Interscience, Hoboken

    Book  Google Scholar 

  5. Alba E, Bernabé Dorronsoro (2010) Cellular genetic algorithms. Springer, New York

    MATH  Google Scholar 

  6. Alba E, Giacobini M, Tomassini M, Romero S (2002) Comparing synchronous and asynchronous cellular genetic algorithms. In: International Conference on Parallel Problem Solving from Nature. Springer, Berlin, Heidelberg

  7. Álvarez JD, O FCDL, García Martínez JÁ, et al (2017) Estimating energy consumption in evolutionary algorithms by means of FRBS. In: Progress in Artificial Intelligence Lecture Notes in Computer Science, pp 229–240

  8. Bán D, Ferenc R, Siket I et al (2018) Prediction models for performance, power, and energy efficiency of software executed on heterogeneous hardware. J Supercomput 2018:1–25

    Google Scholar 

  9. Calandrini G, Gardel A, Bravo I et al (2013) Power measurement methods for energy efficient applications. Sensors 13:7786–7796

    Article  Google Scholar 

  10. David H, Gorbatov E, Hanebutte UR, Khanaa R, Le C (2010) Rapl. In: Proceedings of the 16th ACM/IEEE International Symposium on Low Power Electronics and Design—ISLPED 10

  11. Dorronsoro B, Burguillo JC, Peleteiro A, Bouvry P (2013) Evolutionary algorithms based on game theory and cellular automata with coalitions. In: Zelinka I, Snášel V, Abraham A (eds) Handbook of optimization intelligent systems reference library. Springer, Berlin, pp 481–503

    Google Scholar 

  12. Droste S, Jansen T, Wegener I (2000) A natural and simple function which is hard for all evolutionary algorithms. In: 2000 26th Annual Conference of the IEEE Industrial Electronics Society IECON 2000 IEEE International Conference on Industrial Electronics, Control and Instrumentation 21st Century Technologies and Industrial Opportunities (Cat No00CH37141)

  13. Escamilla J, Salido MA, Giret A, Barber F (2016) A metaheuristic technique for energy-efficiency in job-shop scheduling. Knowl Eng Rev 31:475–485

    Article  Google Scholar 

  14. Fanfakh A, Charr J-C, Couturier R, Giersch A (2017) Energy consumption reduction for asynchronous message-passing applications. J Supercomput 73:2369–2401

    Article  Google Scholar 

  15. Goldberg D, Deb K, Horn J (1992) Massive multimodality, deception, and genetic algorithms. In: Manner R, Manderick B (eds) International Conference on Parallel Problem Solving from Nature II

  16. Guzman C, Cardenas A, Agbossou K (2017) Evaluation of meta-heuristic optimization methods for home energy management applications. In: 2017 IEEE 26th International Symposium on Industrial Electronics (ISIE)

  17. Hähnel M, Döbel B, Völp M, Härtig H (2012) Measuring energy consumption for short code paths using RAPL. ACM SIGMETRICS Perform Eval Rev 40:13

    Article  Google Scholar 

  18. Hindle A (2016) Green software engineering: the curse of methodology. In: Proceedings: IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering (SANER)

  19. Hooper A (2008) Green computing. Commun ACM 51(10):11–13

    Article  Google Scholar 

  20. Jong KD, Potter M, Spears W (1997) Using problem generators to explore the effects of epistasis. In: The Seventh International Conference on Genetic Algorithms, pp 338–345

  21. Khan KN, Ou Z, Hirki M et al (2016) How much power does your server consume? Estimating wall socket power using RAPL measurements. Comput Sci Res Dev 31:207–214

    Article  Google Scholar 

  22. Khuri S, Bäck T, Heitkötter J (1994) An evolutionary approach to combinatorial optimization problems. In: 22nd Annual ACM C.S. Conference, pp 66–73

  23. MacWilliams F, Sloane N (1977) The theory of error-correcting codes: part 2, vol 16. Elsevier, Amsterdam

    MATH  Google Scholar 

  24. Martín G, Singh DE, Marinescu M-C, Carretero J (2015) Enhancing the performance of malleable MPI applications by using performance-aware dynamic reconfiguration. Parallel Comput 46:60–77

    Article  Google Scholar 

  25. Mezmaz M, Melab N, Kessaci Y et al (2011) A parallel bi-objective hybrid metaheuristic for energy-aware scheduling for cloud computing systems. J Parallel Distrib Comput 71:1497–1508

    Article  Google Scholar 

  26. Michaelides EE (2012) Environmental and ecological effects of energy production and consumption. In: Green Energy and Technology Alternative Energy Sources, pp 33–63

  27. Munawer ME (2018) Human health and environmental impacts of coal combustion and post-combustion wastes. J Sustain Min 17:87–96

    Article  Google Scholar 

  28. Pereira R, Couto M, Ribeiro F, et al (2017) Energy efficiency across programming languages: how do energy, time, and memory relate? In: Proceedings of the 10th ACM SIGPLAN International Conference on Software Language Engineering—SLE 2017

  29. Rada-Vilela J, Zhang M, Seah W (2013) A performance study on synchronicity and neighborhood size in particle swarm optimization. Soft Comput 17:1019–1030

    Article  Google Scholar 

  30. Rauber T, Rünger G, Schwind M et al (2014) Energy measurement, modeling, and prediction for processors with frequency scaling. J Supercomput 70:1451–1476

    Article  Google Scholar 

  31. Rodriguez-Gonzalo M, Singh DE, Blas JG, Carretero J (2016) Improving the energy efficiency of MPI applications by means of malleability. In: 2016 24th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP)

  32. Rotem E, Naveh A, Ananthakrishnan A et al (2012) Power-management architecture of the intel microarchitecture code-named sandy bridge. IEEE Micro 32:20–27

    Article  Google Scholar 

  33. Schaffer J, Eshelman L (1991) On crossover as an evolutionary viable strategy. In: Belew R, Booker L (eds) Proceedings of the 4th ICGA, Morgan Kaufmann, pp 61–68

  34. Tomassini M (2006) Spatially structured evolutionary algorithms: artificial evolution in space and time. Springer, Berlin

    MATH  Google Scholar 

  35. Stinson D (1985) An introduction to the design and analysis of algorithms. The Charles Babbage Research Centre, St Pierre

    Google Scholar 

  36. Trefethen AE, Thiyagalingam J (2013) Energy-aware software: challenges, opportunities and strategies. J Comput Sci 4:444–449

    Article  Google Scholar 

  37. Tsutsui S, Fujimoto Y (1993) Forking genetic algorithm with blocking and shrinking modes. In: Forrest S (ed) 5th ICGA, Morgan Kaufmamann, pp 206–213

  38. Vega FFD, Chávez F, Díaz J et al (2016) A cross-platform assessment of energy consumption in evolutionary algorithms. In: Parallel Problem Solving from Nature—PPSN XIV Lecture Notes in Computer Science, pp 548–557

  39. Venkatesh A, Kandalla K, Panda DK (2013) Evaluation of energy characteristics of MPI communication primitives with RAPL. In: 2013 IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum

  40. Venter G, Sobieszczanski-Sobieski J (2006) Parallel particle swarm optimization algorithm accelerated by asynchronous evaluations. J Aerosp Comput Inf Commun 3:123–137

    Article  Google Scholar 

  41. Zhang H, Hoffman H (2015) A quantitative evaluation of the RAPL power control system. In: Feedback Computing

Download references

Acknowledgements

This research has been partially funded by the Spanish MINECO and FEDER projects TIN2016-81766-REDT (CI-RTI), TIN2017-88213-R (6city), and Andalucía Tech, Universidad de Málaga.

Author information

Authors and Affiliations

  1. Dpto. de Lenguajes y Ciencias de la Computación, Univ. de Málaga, E.T.S. Ingeniería Informática, Campus de Teatinos, 29071, Málaga, Spain

    Amr Abdelhafez, Enrique Alba & Gabriel Luque

Authors
  1. Amr Abdelhafez
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Enrique Alba
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gabriel Luque
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Amr Abdelhafez.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Abdelhafez, A., Alba, E. & Luque, G. A component-based study of energy consumption for sequential and parallel genetic algorithms. J Supercomput 75, 6194–6219 (2019). https://doi.org/10.1007/s11227-019-02843-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-019-02843-4

Keywords

Search

Navigation