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Analysis of semi-asynchronous multi-objective evolutionary algorithm with different asynchronies

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Abstract

This paper proposes a novel master–slave parallel evolutionary algorithm (EA) approach with different asynchrony and provides its detailed analyses on multi-objective optimization problems. We express the proposed EA with different asynchrony as a semi-asynchronous EA. A semi-asynchronous EA generates new solutions whenever evaluations of the predefined number of solutions complete, unlike a conventional synchronous EA waits for evaluations of all solutions to generate the next population. To establish a semi-asynchronous EA, this paper introduces an asynchrony parameter that is used to decide how many solutions are waited to generate new solutions. We conduct an experiment to verify the effectiveness of the proposed semi-asynchronous EA on benchmark problems with the several variances of the evaluation time. In the experiment, we apply a semi-asynchronous EA to NSGA-II and NSGA-III, which are well-known multi-objective EAs. The semi-asynchronous NSGA-IIs and the semi-asynchronous NSGA-IIIs with different asynchronies are compared on multi-objective optimization benchmark problems. The experimental result reveals that semi-asynchronous approaches with an appropriate asynchrony have possibility to outperform the asynchronous and the synchronous ones. Additionally, detailed analysis reveals that an appropriate asynchrony may vary not only depends on a target problem but also depends on the degree of the evolution process.

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Notes

  1. \(\lceil x\rceil \) indicates the ceiling function that maps a real number x to the smallest next integer.

References

  • Akay B (2013) Synchronous and asynchronous Pareto-based multi-objective artificial bee colony algorithms. J Glob Optim 57(2):415–445. https://doi.org/10.1007/s10898-012-9993-1

    Article  MathSciNet  MATH  Google Scholar 

  • Beume N, Naujoks B, Emmerich M (2007) Sms-emoa: multiobjective selection based on dominated hypervolume. Eur J Oper Res 181(3):1653–1669. https://doi.org/10.1016/j.ejor.2006.08.008

    Article  MATH  Google Scholar 

  • Carlisle A, Dozier G (2001) An off-the-shelf PSO. In: Particle swarm optimization workshop. Technology IUPUI, Indianapolis, IN, pp 1–6

  • Chang JF, Chu SC, Roddick JF, Pan JS (2005) A parallel particle swarm optimization algorithm with communication strategies. J Inf Sci Eng 21(4):809–818

    Google Scholar 

  • Chipperfield A, Fleming P (1996) Parallel genetic algorithm. In: Zomaya AY (ed) Parallel and distributed computing handbook. McGraw-Hill, pp 1118–1143

  • Deb K, Agrawal RB (1995) Simulated binary crossover for continuous search space. Complex Syst 9:115–148

    MathSciNet  MATH  Google Scholar 

  • Deb K, Goyal M (1996) A combined genetic adaptive search (geneas) for engineering design. Comput Sci Inform 26:30–45

    Google Scholar 

  • Deb K, Jain H (2014) An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part i: solving problems with box constraints. IEEE Trans Evolut Comput 18(4):577–601. https://doi.org/10.1109/TEVC.2013.2281535

    Article  Google Scholar 

  • Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197. https://doi.org/10.1109/4235.996017

    Article  Google Scholar 

  • Depolli M, Trobec R, Filipic B (2013) Asynchronous master–slave parallelization of differential evolution for multi-objective optimization. Evol Comput 21(2):261–291

    Article  Google Scholar 

  • Durillo JJ, Nebro AJ, Luna F, Alba E (2008) A study of master–slave approaches to parallelize nsga-ii. In: 2008 IEEE international symposium on parallel and distributed processing, pp 1–8. https://doi.org/10.1109/IPDPS.2008.4536375

  • Durillo JJ, Zhang Q, Nebro AJ, Alba E (2011) Distribution of computational effort in parallel MOEA/D. In: Coello CAC (ed) Learning and intelligent optimization, vol 6683. Lecture notes in computer science. Springer, Berlin, pp 488–502. https://doi.org/10.1007/978-3-642-25566-3_38

    Chapter  Google Scholar 

  • Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning, 1st edn. Addison-Wesley Longman Publishing Co., Inc, Boston

    MATH  Google Scholar 

  • Gong YJ, Chen WN, Zhan ZH, Zhang J, Li Y, Zhang Q, Li JJ (2015) Distributed evolutionary algorithms and their models: a survey of the state-of-the-art. Appl Soft Comput 34:286–300. https://doi.org/10.1016/j.asoc.2015.04.061

    Article  Google Scholar 

  • Harada T, Takadama K (2013) Asynchronous evaluation based genetic programming: comparison of asynchronous and synchronous evaluation and its analysis. In: Krawiec K, Moraglio A, Hu T, Etaner-Uyar A, Hu B (eds) Genetic programming, vol 7831. Lecture notes in computer science. Springer, Berlin, pp 241–252. https://doi.org/10.1007/978-3-642-37207-0_21

    Chapter  Google Scholar 

  • Harada T, Takadama K (2014) Asynchronously evolving solutions with excessively different evaluation time by reference-based evaluation. In: GECCO ’14: proceedings of the 2014 conference on genetic and evolutionary computation. ACM, Vancouver, BC, Canada, pp 911–918. https://doi.org/10.1145/2576768.2598330

  • Harada T, Takadama K (2017a) Performance comparison of parallel asynchronous multi-objective evolutionary algorithm with different asynchrony. In: 2017 IEEE congress on evolutionary computation (CEC), pp 1215–1222. https://doi.org/10.1109/CEC.2017.7969444

  • Harada T, Takadama K (2017b) A study of self-adaptive semi-asynchronous evolutionary algorithm on multi-objective optimization problem. In: Proceedings of the genetic and evolutionary computation conference companion, GECCO ’17. ACM, New York, NY, USA, pp 1812–1819. https://doi.org/10.1145/3067695.3084221

  • Holm S (1979) A simple sequentially rejective multiple test procedure. Scand J Stat 6(2):65–70

    MathSciNet  MATH  Google Scholar 

  • Huband S, Barone L, While L, Hingston P (2005) A scalable multi-objective test problem toolkit. Springer, Berlin, pp 280–295. https://doi.org/10.1007/978-3-540-31880-4_20

    Book  MATH  Google Scholar 

  • Jain H, Deb K (2014) An evolutionary many-objective optimization algorithm using reference-point based nondominated sorting approach, part ii: handling constraints and extending to an adaptive approach. IEEE Trans Evol Comput 18(4):602–622. https://doi.org/10.1109/TEVC.2013.2281534

    Article  Google Scholar 

  • Koh BI, George AD, Haftka RT, Fregly BJ (2006) Parallel asynchronous particle swarm optimization. Int J Numer Methods Eng 67(4):578–595. https://doi.org/10.1002/nme.1646

    Article  MATH  Google Scholar 

  • Koza J (1992) Genetic programming on the programming of computers by means of natural selection. MIT Press, Cambridge

    MATH  Google Scholar 

  • Kruskal WH, Wallis WA (1952) Use of ranks in one-criterion variance analysis. J Am Stat Assoc 47(260):583–621. https://doi.org/10.2307/2280779

    Article  MATH  Google Scholar 

  • Lewis A, Mostaghim S, Scriven I (2009) Asynchronous multi-objective optimisation in unreliable distributed environments. In: Lewis A, Mostaghim S, Randall M (eds) Biologically-inspired optimisation methods, studies in computational intelligence, vol 210. Springer, Berlin, pp 51–78. https://doi.org/10.1007/978-3-642-01262-4_3

    Chapter  Google Scholar 

  • Luna F, Zavala GR, Nebro AJ, Durillo JJ, Coello CAC (2016) Distributed multi-objective metaheuristics for real-world structural optimization problems. Comput J 59(6):777–792. https://doi.org/10.1093/comjnl/bxu082

    Article  Google Scholar 

  • Maxwell III SR (1994) Experiments with a coroutine model for genetic programming. In: Proceedings of the 1994 IEEE world congress on computational intelligence, vol 1. IEEE Press, Orlando, Florida, USA, pp 413–417a. https://doi.org/10.1109/ICEC.1994.349915.http://ieeexplore.ieee.org/iel2/1125/8059/00349915.pdf?isNumber=8059. Accessed 19 May 2019

  • Milani A, Santucci V (2010) Asynchronous differential evolution. In: Proceedings of the 2010 IEEE congress on evolutionary computation (CEC 2010), pp 1–7 . https://doi.org/10.1109/CEC.2010.5586107

  • Nebro AJ, Durillo JJ, Vergne M (2015) Redesigning the jmetal multi-objective optimization framework. In: Proceedings of the companion publication of the 2015 annual conference on genetic and evolutionary computation, GECCO Companion ’15. ACM, New York, NY, USA, pp 1093–1100. https://doi.org/10.1145/2739482.2768462

  • Obayashi S, Jeong S, Shimoyama K, Chiba K, Morino H (2010) Multi-objective design exploration and its applications. Int J Aeronaut Space Sci 4(4) . https://doi.org/10.5139/IJASS.2010.11.4.247

    Article  Google Scholar 

  • Robič T, Filipič B (2005) DEMO: differential evolution for multiobjective optimization. Springer, Berlin, pp 520–533. https://doi.org/10.1007/978-3-540-31880-4_36

    Book  MATH  Google Scholar 

  • Santander-Jiménez S, Vega-Rodríguez MA (2016a) Asynchronous non-generational model to parallelize metaheuristics: a bioinformatics case study. IEEE Trans Parallel Distrib Syst 28(7):1825–1838. https://doi.org/10.1109/TPDS.2016.2645764

    Article  Google Scholar 

  • Santander-Jiménez S, Vega-Rodríguez MA (2016b) Performance evaluation of dominance-based and indicator-based multiobjective approaches for phylogenetic inference. Inf Sci 330(C):293–314. https://doi.org/10.1016/j.ins.2015.10.021

    Article  Google Scholar 

  • Scott EO, De Jong KA (2015a) Evaluation-time bias in asynchronous evolutionary algorithms. In: Proceedings of the companion publication of the 2015 annual conference on genetic and evolutionary computation, GECCO Companion ’15. ACM, New York, NY, USA, pp 1209–1212. https://doi.org/10.1145/2739482.2768482

  • Scott EO, De Jong KA (2015b) Understanding simple asynchronous evolutionary algorithms. In: Proceedings of the 2015 ACM conference on foundations of genetic algorithms XIII, FOGA ’15. ACM, New York, NY, USA, pp 85–98. https://doi.org/10.1145/2725494.2725509

  • Tasoulis DK, Pavlidis NG, Plagianakos VP, Vrahatis MN (2004) Parallel differential evolution. In: Proceedings of the 2004 congress on evolutionary computation (IEEE Cat. No.04TH8753), vol 2, pp 2023–2029. https://doi.org/10.1109/CEC.2004.1331145

  • Wessing S, Rudolph G, Menges DA (2016) Comparing asynchronous and synchronous parallelization of the SMS-EMOA. Springer, Cham, pp 558–567. https://doi.org/10.1007/978-3-319-45823-6_52

    Book  Google Scholar 

  • Wilcoxon F (1945) Individual comparisons by ranking methods. Biom Bull 1(6):80–83

    Article  Google Scholar 

  • Zhabitskaya E, Zhabitsky M (2013) Asynchronous differential evolution with restart. In: Dimov I, Farag I, Vulkov L (eds) Numerical analysis and its applications, vol 8236. Lecture notes in computer science. Springer, Berlin, pp 555–561. https://doi.org/10.1007/978-3-642-41515-9_64

    Chapter  Google Scholar 

  • Zitzler E, Thiele L (1998) Multiobjective optimization using evolutionary algorithms—a comparative case study. Springer, Berlin, pp 291–301. https://doi.org/10.1007/BFb0056872

    Book  Google Scholar 

  • Zitzler E, Deb K, Thiele L (2000) Comparison of multiobjective evolutionary algorithms: empirical results. Evol Comput 8(2):173–195

    Article  Google Scholar 

  • Zitzler E, Laumanns M, Thiele L (2001) SPEA2: improving the strength pareto evolutionary algorithm. TIK report 103, Computer Engineering and Networks Laboratory (TIK), ETH Zurich, Zurich, Switzerland

  • Zăvoianu AC, Lughofer E, Koppelstätter W, Weidenholzer G, Amrhein W, Klement EP (2015) Performance comparison of generational and steady-state asynchronous multi-objective evolutionary algorithms for computationally-intensive problems. Knowl Based Syst 87(C):47–60. https://doi.org/10.1016/j.knosys.2015.05.029

    Article  Google Scholar 

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Acknowledgements

This work was supported by JSPS Grant-in-Aid for Scientific Research (B) Grant Number JP16KT0103.

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Correspondence to Tomohiro Harada.

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Harada, T., Takadama, K. Analysis of semi-asynchronous multi-objective evolutionary algorithm with different asynchronies. Soft Comput 24, 2917–2939 (2020). https://doi.org/10.1007/s00500-019-04071-7

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