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Maurice Clerc (mathematician)

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Maurice Clerc
Born1949/04/24
NationalityFrench
Occupation(s)Mathematician and writer

Biography

Family and et studies

Maurice Marcel Clerc was born on April 24, 1949. He obtained his engineering degree in 1972, at the end of his graduate studies at the Institut industriel du Nord (renamed Centrale Lille in 1991) [1].

Professional career

Maurice Clerc works in the Research and Development department of France Télécom.

His early work focused on fuzzy representations[2] · [3] · [4]. He is then recognized as a global specialist in particle swarm optimization (PSO) [5] · [6] jointly with avec James Kennedy. He is, together with the latter, lead co-author of the first detailed theoretical analysis of this method[7], rewarded by IEEE in 2005[8].

This article is in fact only the format of a study published online since 1999. He outlines and demonstrates convergence theorems in a five-dimensional space and, in particular, defines the constriction concept that has been largely used since in the context of PSO.

In the early PSO versions of the positions of particles tended to “explode” to very large values, except to be constrained by arbitrarily limiting their speed.

Noticing periodic regularities in deterministic particle trajectories, Clerc analyses the dynamics of the stochastic system and proves that the trajectory of a particle can be controlled in a more sophisticated way, by applying a constriction coefficient, which allows the particle to adjust its search distance, decreasing it if necessary or increasing it if a new region is found.

The analysis defines value intervals for the coefficients, which make it possible to generalize the algorithm to many types of problems. Thus a researcher or an application developer can use the same model, with the same coefficients, without arbitrary limitation of speeds.

From the same basic code, simply define the objective function of the problem to be treated. A more versatile approach than previous ones, which is now used in virtually all PSO variants. By the end of 2020 more than a thousand other authors' publications are referring to it[9].

Clerc introduces other innovations in the swarm intelligence paradigm, for example with the «Swarm and Queen» approach[10] ou with variable topology through which the population structure is modified in an adaptive way during the algorithm run.

His work on the use of the PSO for combinatorial problems, such as the Commercial Traveler, is particularly innovative in that he completely redefines the concepts of “speed” and “distance” to address these types of problems[11].

Its definition of the concept of stagnation and its analysis have also led to improvements in the algorithm[12]

He works with many people around the world. In particular James Kennedy (see above), Riccardo Poli on the XPS (eXtended Particle Swarms)[13] project of the University of Essex, Patrick Siarry, professor at the University Paris-Est Créteil[14]·[15], Mahamed G. H. Omran, professor at the Gulf University du Koweït (Adaptive Population-based Simplex)[16] · [17], several teacher-researchers in India, including the Indian Institute of Technology (IIT) in Roorkee[18], etc.

He is involved in updating the Particle Swarm Central site[19].

Retired since 2004, he remains active in various fields of research, related to PSO or not[20]·[21] publications of articles and books, keynote speaker in conferences[22] · [23], director and thesis jury[24] · [25]. He also occasionally works as an optimization consultant[26] · [27].

Publications

Books

Maurice Clerc is the author of three books:

  • Particle Swarm Optimization[28]
  • Guided Randomness in Optimization[29]
  • Iterative Optimizers[30]

Articles and conference texts

  • (2001) "Think locally, act locally: The Way of Life of A2PSO, and Adaptive Particle Swarm Optimizer" , France Télécom R&D.
  • "Stagnation Analysis in Particle Swarm Optimisation or What Happens When Nothing Happens", Technical report, University of Essex,‎ 2006.
  • "Why does it work?", International Journal of Computational Intelligence Research 4, n° 2,‎ 2008, p. 79-91.
  • "Beyond Standard Particle Swarm Optimisation", International Journal of Swarm Intelligence Research 4,‎ 2010, p. 46-66.
  • Handbook of Swarm Intelligence, vol. 8, Heidelberg, Springer, 2011 (ISBN 978-3-642-26689-8), "From Theory to Practice in Particle Swarm Optimization", p. 3-36.
  • "Cooperation Mechanisms in Particle Swarm Optimisation", Nature Inspired Computing : Theory and Industrial Application,‎ 2013.
  • "List Based Optimisers - Experiments and Open Questions", International Journal of Swarm Intelligence Research, vol. 4(4),‎ 2014.
  • "Total memory optimiser: proof of concept and compromises", International Journal of Swarm Intelligence, vol. 3,‎ 2017.

Personal technical site

Notes and references

  1. ^ Répertoire des Ingénieurs et scientifiques de France.
  2. ^ Maurice Clerc, François Guérin et Xavier Chanet. « Représentations floues dans un mémoriel ». dans JIOSC (Journées internationales d'Orsay sur les sciences cognitives), édité par CNRS, 211‑22. Orsay, France: CNRS, 1994.
  3. ^ Earl D. Cox; (trad.) Maurice Clerc (1995). "La logique floue pour les affaires et l'industrie". momox-shop. Retrieved 2021-12-28.
  4. ^ Clerc, Maurice (1996). "Déduction en Représentation Floue Hiérarchique. Un exemple". In Cognito. 3.
  5. ^ M. Clerc (2006). Particle Swarm Optimization. {{cite book}}: Unknown parameter |agency= ignored (help)
  6. ^ Clerc, Maurice (2015). Guided Randomness in Optimization. {{cite book}}: Unknown parameter |agency= ignored (help)
  7. ^ Clerc, M.; Kennedy, J. (2002). "The particle swarm - explosion, stability, and convergence in a multidimensional complex space". IEEE Transactions on Evolutionary Computation. 6 (1): 58–73. doi:10.1109/4235.985692. ISSN 1089-778X.
  8. ^ "Past Recipients - IEEE Computational Intelligence Society". cis.ieee.org. Retrieved 2021-11-14..
  9. ^ Baas, Jeroen; Boyack, Kevin; Ioannidis, John P. A. (2021-10-19). "August 2021 data-update for "Updated science-wide author databases of standardized citation indicators"". 3. doi:10.17632/btchxktzyw.3. {{cite journal}}: Cite journal requires |journal= (help)
  10. ^ Clerc, M. "The swarm and the queen: towards a deterministic and adaptive particle swarm optimization". Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406). IEEE. doi:10.1109/cec.1999.785513.
  11. ^ Clerc, Maurice (2004), "Discrete Particle Swarm Optimization, illustrated by the Traveling Salesman Problem", New Optimization Techniques in Engineering, Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 219–239, ISBN 978-3-642-05767-0, retrieved 2021-12-31
  12. ^ Verfasser, Schmitt, Berthold Immanuel. Convergence analysis for particle swarm optimization. ISBN 978-3-944057-30-9. OCLC 908646543. {{cite book}}: |last= has generic name (help)CS1 maint: multiple names: authors list (link)
  13. ^ "XPS Particle Swarm". xps-swarm.essex.ac.uk. Retrieved 2021-11-15.
  14. ^ Siarry, Patrick, ed. (2016). "Metaheuristics". doi:10.1007/978-3-319-45403-0. {{cite journal}}: Cite journal requires |journal= (help)
  15. ^ Clerc, Maurice; Siarry, Patrick (2004). "Une nouvelle métaheuristique pour l'optimisation difficile : la méthode des essaims particulaires". J3eA. 3: 007. doi:10.1051/bib-j3ea:2004007. ISSN 1638-5705.
  16. ^ Omran, Mahamed G. H.; Clerc, Maurice (2017-08-30). "APS 9: an improved adaptive population-based simplex method for real-world engineering optimization problems". Applied Intelligence. 48 (6): 1596–1608. doi:10.1007/s10489-017-1015-z. ISSN 0924-669X.
  17. ^ "Welcome — Adaptive Population based Simplex". aps-optim.info. Retrieved 2021-11-14.
  18. ^ Bansal, Jagdish Chand; Sharma, Harish; Jadon, Shimpi Singh; Clerc, Maurice (2014-01-01). "Spider Monkey Optimization algorithm for numerical optimization". Memetic Computing. 6 (1): 31–47. doi:10.1007/s12293-013-0128-0. ISSN 1865-9284.
  19. ^ "Particle Swarm Central". particleswarm.info. Retrieved 2021-11-14.
  20. ^ Clerc, Maurice; Dattasharma, Abhi (2014). "Comparisons in optimisation: the Eff-Res approach". International Journal of Swarm Intelligence. 1 (3): 266. doi:10.1504/ijsi.2014.066557. ISSN 2049-4041.
  21. ^ Clerc, Maurice (2020). A general quantum method to solve the graph K-colouring problem.
  22. ^ "SocProS'19". socpros19.scrs.in. Retrieved 2021-11-14.
  23. ^ "Keynote speakers | CIS 2020". www.cis2020.scrs.in. Retrieved 2021-11-14.
  24. ^ Cooren, Yann (2008-11-27). Perfectionnement d'un algorithme adaptatif d'optimisation par essaim particulaire : application en génie médical et en électronique (These de doctorat thesis). Paris Est.
  25. ^ Mauro Sebastian, Innocente (2010). "Development and testing of a particle swarm optimizer to handle hard unconstrained and constrained problems". cronfa.swan.ac.uk. Retrieved 2021-11-15.
  26. ^ Altman, Zwi; Sallem, Soumaya; Nasri, Ridha; Sayrac, Berna; Clerc, Maurice (2014). "Particle swarm optimization for Mobility Load Balancing SON in LTE networks". 2014 IEEE Wireless Communications and Networking Conference Workshops (WCNCW): 172–177. doi:10.1109/WCNCW.2014.6934881.
  27. ^ Salman, Ayed; Omran, Mahamed G. H.; Clerc, Maurice; Alsharhan, Salah (2016-01-01). "Improving the performance of comprehensive learning particle swarm optimizer". Journal of Intelligent & Fuzzy Systems. 30 (2): 735–746. doi:10.3233/IFS-151794. ISSN 1064-1246.
  28. ^ Clerc, Maurice (2006-01-01). Particle Swarm Optimization. London, UK: ISTE. ISBN 978-0-470-61216-3.
  29. ^ Maurice., Clerc, (2015). Guided randomness in optimization. Wiley-ISTE. ISBN 978-1-119-13645-3. OCLC 910670595.{{cite book}}: CS1 maint: extra punctuation (link) CS1 maint: multiple names: authors list (link)
  30. ^ Clerc, Maurice (2019-04-08). Iterative Optimizers. Wiley. ISBN 978-1-78630-409-4.
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