Abstract
Parallel evolutionary algorithms (PEAs) have been studied for reducing the execution time of evolutionary algorithms by utilizing parallel computing. An asynchronous PEA (APEA) is a scheme of PEAs that increases computational efficiency by generating a new solution immediately after a solution evaluation completes without the idling time of computing nodes. However, because APEA gives more search opportunities to solutions with shorter evaluation times, the evaluation time bias of solutions negatively affects the search performance. To overcome this drawback, this paper proposes a new parent selection method to reduce the effect of evaluation time bias in APEAs. The proposed method considers the search frequency of solutions and selects the parent solutions so that the search progress in the population is uniform regardless of the evaluation time bias. This paper conducts experiments on multi-objective optimization problems that simulate the evaluation time bias. The experiments use NSGA-III, a well-known multi-objective evolutionary algorithm, and compare the proposed method with the conventional synchronous/asynchronous parallelization. The experimental results reveal that the proposed method can reduce the effect of the evaluation time bias while reducing the computing time of the parallel NSGA-III.
Similar content being viewed by others
Notes
This work does not use MMF1 and MMF7 because they have a continuous, non-separate Pareto set.
References
Abbasi M, Rafiee M, Khosravi MR, Jolfaei A, Menon VG, Koushyar JM (2020) An efficient parallel genetic algorithm solution for vehicle routing problem in cloud implementation of the intelligent transportation systems. J Cloud Comput 9(1):6
Alba E, Luque G, Nesmachnow S (2013) Parallel metaheuristics: recent advances and new trends. Int Trans Oper Res 20(1):1–48. https://doi.org/10.1111/j.1475-3995.2012.00862.x
Chitty DM (2021) A partially asynchronous global parallel genetic algorithm. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion. GECCO ’21, pp. 1771–1778. Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3449726.3463190
Coello CAC, Cortés NC (2005) Solving multiobjective optimization problems using an artificial immune system. Genet Program Evol Mach 6(2):163–190. https://doi.org/10.1007/s10710-005-6164-x
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 Evol Comput 18(4):577–601. https://doi.org/10.1109/TEVC.2013.2281535
Depolli M, Trobec R, Filipič B (2013) Asynchronous master-slave parallelization of differential evolution for multi-objective optimization. Evol Comput 21(2):261–291. https://doi.org/10.1162/EVCO_a_00076
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
Geetha P, Nanda SJ, Yadav RP (2022) A parallel chaotic sailfish optimization algorithm for estimation of DOA in wireless sensor array. Phys Commun 51:101536. https://doi.org/10.1016/j.phycom.2021.101536
Harada T (2020) Search progress dependent parent selection for avoiding evaluation time bias in asynchronous parallel multi-objective evolutionary algorithms. In: 2020 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1013–1020. https://doi.org/10.1109/SSCI47803.2020.9308152
Harada T, Alba E (2020) Parallel genetic algorithms: a useful survey. ACM Comput Surv. https://doi.org/10.1145/3400031
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 AŞ, Hu B (eds) Genet Progr. Springer, Berlin, Heidelberg, pp 241–252
Harada T, Takadama K (2020) Analysis of semi-asynchronous multi-objective evolutionary algorithm with different asynchronies. Soft Comput 24(4):2917–2939. https://doi.org/10.1007/s00500-019-04071-7
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
Nguyen T, Bui T, Fujita H, Hong T-P, Loc HD, Snasel V, Vo B (2021) Multiple-objective optimization applied in extracting multiple-choice tests. Eng Appl Artif Intell 105:104439. https://doi.org/10.1016/j.engappai.2021.104439
Raghul S, Jeyakumar G (2022) Parallel and distributed computing approaches for evolutionary algorithms—a review. In: Sharma TK, Ahn CW, Verma OP, Panigrahi BK (eds) Soft Comput: Theor Appl. Springer, Singapore, pp 433–445
Scott EO, De Jong KA (2015) 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, pp. 1209–1212. ACM, New York, NY, USA. https://doi.org/10.1145/2739482.2768482
Scott EO, De Jong KA (2015) Understanding simple asynchronous evolutionary algorithms. In: Proceedings of the 2015 ACM Conference on Foundations of Genetic Algorithms XIII. FOGA ’15, pp. 85–98. ACM, New York, NY, USA. https://doi.org/10.1145/2725494.2725509
Shayeghi A, Gotz D, Davis JBA, Schafer R, Johnston RL (2015) Pool-BCGA: a parallelised generation-free genetic algorithm for the ab initio global optimisation of nanoalloy clusters. Phys Chem Chem Phys 17:2104–2112. https://doi.org/10.1039/C4CP04323E
Soufan O, Kleftogiannis D, Kalnis P, Bajic VB (2015) DWFS: a wrapper feature selection tool based on a parallel genetic algorithm. PLoS ONE 10(2):1–23. https://doi.org/10.1371/journal.pone.0117988
Wessing S, Rudolph G, Menges DA (2016) Comparing asynchronous and synchronous parallelization of the SMS-EMOA. In: Handl J, Hart E, Lewis PR, López-Ibáñez M, Ochoa G, Paechter B (eds) Parallel Prob Solv Nat- PPSN XIV. Springer, Cham, pp 558–567
Yue C, Qu B, Liang J (2018) A multiobjective particle swarm optimizer using ring topology for solving multimodal multiobjective problems. IEEE Trans Evol Comput 22(5):805–817. https://doi.org/10.1109/TEVC.2017.2754271
Zăvoianu A-C, 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 87:47–60. https://doi.org/10.1016/j.knosys.2015.05.029
Zhabitskaya E, Zhabitsky M (2013) Asynchronous differential evolution with restart. In: Dimov I, Faragó I, Vulkov L (eds) Numer Anal Appl. Springer, Berlin, Heidelberg, pp 555–561
Zhou A, Zhang Q, Jin Y (2009) Approximating the set of pareto-optimal solutions in both the decision and objective spaces by an estimation of distribution algorithm. IEEE Trans Evol Comput 13(5):1167–1189. https://doi.org/10.1109/TEVC.2009.2021467
Funding
This work was supported by Japan Society for the Promotion of Science Grant-in-Aid for Young Scientists Grant Number JP19K20362.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The author declares that he has no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Harada, T. A frequency-based parent selection for reducing the effect of evaluation time bias in asynchronous parallel multi-objective evolutionary algorithms. Nat Comput (2022). https://doi.org/10.1007/s11047-022-09940-z
Accepted:
Published:
DOI: https://doi.org/10.1007/s11047-022-09940-z