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Neural Computing and Applications

Erschienen in:

18.07.2018 | S.I.: India Intl. Congress on Computational Intelligence 2017

Development of a framework for modeling preference times in triathlon

verfasst von: Iztok Fister Jr., Andres Iglesias, Suash Deb, Dušan Fister, Iztok Fister

Erschienen in: Neural Computing and Applications | Ausgabe 15/2020

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Abstract

Preference time in a triathlon denotes the time that is planned to be achieved by an athlete in a particular competition. Usually, the preference time is calculated some days, weeks, or even months before the competition. Mostly, trainers calculate the proposed preference time according to the current form, body performances of athletes, psychological abilities and their health state. They also take course specifications into account in order to make their proposal as exact as possible. However, until recently, this prediction was performed manually. This paper presents an automatic framework for modeling preference times based on previous results of athletes on a particular racecourse and particle swarm optimization. Indeed, the framework observed the problem as optimization, where the goal is to find such preference time that is as much as possible correlated with past data. Practical experiments with different scenarios reveal that the proposed solution is promising.

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www.ironman.com.

Data from 2017 editions are not included in dataset edition of 2016.

Swarm plots were created by seaborn python package: https://github.com/mwaskom/seaborn.

Abdel-Basset M, Abdle-Fatah L, Sangaiah AK (2018) An improved lévy based whale optimization algorithm for bandwidth-efficient virtual machine placement in cloud computing environment. Clust Comput. https://doi.org/10.1007/s10586-018-1769-z

Abdel-Basset M, Manogaran G, Abdel-Fatah L, Mirjalili S (2018) An improved nature inspired meta-heuristic algorithm for 1-d bin packing problems. Pers Ubiquitous Comput. https://doi.org/10.1007/s00779-018-1132-7

Abdel-Basset M, Shawky LA (2018) Flower pollination algorithm: a comprehensive review. Artif Intell Rev. https://doi.org/10.1007/s10462-018-9624-4

Alatas B, Akin E, Bedri Ozer A (2009) Chaos embedded particle swarm optimization algorithms. Chaos Solitons Fractals 40(4):1715–1734MathSciNetCrossRef

Del Valle Y, Venayagamoorthy GK, Mohagheghi S, Hernandez J-C, Harley RG (2008) Particle swarm optimization: basic concepts, variants and applications in power systems. IEEE Trans Evol Comput 12(2):171–195CrossRef

Fister I, Iglesias A, Deb S, Fister D, Fister I Jr (2017) Modeling preference time in middle distance triathlons. arXiv preprint arXiv:1707.00718

Fister I Jr, Fister D (2016) A collection of ironman, ironman 70.3 and ultra-triathlon race results, version 0.1, technical report 0110

Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks. IEEE, vol 4, pp 1942–1948

Knechtle B, de Sousa CV, Sales MM, Nikolaidis PT (2017) Pacing in deca and double deca iron ultra-triathlon. Adapt Med 9(2):78–84CrossRef

10.

Liao C-J, Tseng C-T, Luarn P (2007) A discrete version of particle swarm optimization for flowshop scheduling problems. Comput Oper Res 34(10):3099–3111CrossRef

11.

Mnadla S, Bragazzi NL, Rouissi M, Chaalali A, Siri A, Padulo J, Ardigò LP, Brigo F, Chamari K, Knechtle B (2016) Infodemiological data of ironman triathlon in the study period 2004–2013. Data Brief 9:123–127CrossRef

12.

Ofoghi B, Zeleznikow J, MacMahon C, Raab M (2013) Data mining in elite sports: a review and a framework. Meas Phys Educ Exerc Sci 17(3):171–186CrossRef

13.

Parsopoulos KE, Vrahatis MN et al (2002) Particle swarm optimization method for constrained optimization problems. Intell Technol Theory Appl N Trends Intell Technol 76(1):214–220MATH

14.

Pearson K (1895) Note on regression and inheritance in the case of two parents. Proc R Soc Lond 58:240–242CrossRef

15.

Peram T, Veeramachaneni K, Mohan CK (2003) Fitness-distance-ratio based particle swarm optimization. In: Proceedings of the 2003 IEEE swarm intelligence symposium. SIS’03. IEEE, pp 174–181

16.

Rana S, Jasola S, Kumar R (2011) A review on particle swarm optimization algorithms and their applications to data clustering. Artif Intell Rev 35(3):211–222CrossRef

17.

Rüst CA, Knechtle B, Knechtle P, Rosemann T, Lepers R (2012) Participation and performance trends in triple iron ultra-triathlon—a cross-sectional and longitudinal data analysis. Asian J Sports Med 3(3):145CrossRef

18.

Shi Y, Eberhart R (1998) A modified particle swarm optimizer. In: The 1998 IEEE international conference on evolutionary computation proceedings. IEEE world congress on computational intelligence. IEEE, pp 69–73

19.

Stiefel M, Knechtle B, Alexander Rüst C, Rosemann T, Lepers R (2013) The age of peak performance in ironman triathlon: a cross-sectional and longitudinal data analysis. Extreme Physiol Med 2(1):27CrossRef

20.

Yang X-S (2010) Nature-inspired metaheuristic algorithms. Luniver Press, Bristol

21.

Zhan Z-H, Zhang J, Li Y, Shi Y-H (2011) Orthogonal learning particle swarm optimization. IEEE Trans Evol Comput 15(6):832–847CrossRef

Titel: Development of a framework for modeling preference times in triathlon
verfasst von: Iztok Fister Jr.
Andres Iglesias
Suash Deb
Dušan Fister
Iztok Fister
Publikationsdatum: 18.07.2018
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 15/2020
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-018-3632-9

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