Convergence analysis of the plant propagation algorithm for continuous global optimization
RAIRO - Operations Research - Recherche Opérationnelle, Tome 52 (2018) no. 2, pp. 429-438.

The Plant Propagation Algorithm (PPA) is a Nature-Inspired stochastic algorithm, which emulates the way plants, in particular the strawberry plant, propagate using runners. It has been experimentally tested both on unconstrained and constrained continuous global optimization problems and was found to be competitive against well established algorithms. This paper is concerned with its convergence analysis. It first puts forward a general convergence theorem for a large class of random algorithms, before the PPA convergence theorem is derived and proved. It then illustrates the results on simple problems.

Reçu le :
Accepté le :
DOI : 10.1051/ro/2017037
Classification : 90C09, 05C15
Mots-clés : Strawberry algorithm, randomised algorithms, convergence analysis, global optimisation
Brahimi, Nassim 1 ; Salhi, Abdellah 1 ; Ourbih-Tari, Megdouda 1

1
@article{RO_2018__52_2_429_0,
     author = {Brahimi, Nassim and Salhi, Abdellah and Ourbih-Tari, Megdouda},
     title = {Convergence analysis of the plant propagation algorithm for continuous global optimization},
     journal = {RAIRO - Operations Research - Recherche Op\'erationnelle},
     pages = {429--438},
     publisher = {EDP-Sciences},
     volume = {52},
     number = {2},
     year = {2018},
     doi = {10.1051/ro/2017037},
     zbl = {1401.90124},
     mrnumber = {3880536},
     language = {en},
     url = {http://archive.numdam.org/articles/10.1051/ro/2017037/}
}
TY  - JOUR
AU  - Brahimi, Nassim
AU  - Salhi, Abdellah
AU  - Ourbih-Tari, Megdouda
TI  - Convergence analysis of the plant propagation algorithm for continuous global optimization
JO  - RAIRO - Operations Research - Recherche Opérationnelle
PY  - 2018
SP  - 429
EP  - 438
VL  - 52
IS  - 2
PB  - EDP-Sciences
UR  - http://archive.numdam.org/articles/10.1051/ro/2017037/
DO  - 10.1051/ro/2017037
LA  - en
ID  - RO_2018__52_2_429_0
ER  - 
%0 Journal Article
%A Brahimi, Nassim
%A Salhi, Abdellah
%A Ourbih-Tari, Megdouda
%T Convergence analysis of the plant propagation algorithm for continuous global optimization
%J RAIRO - Operations Research - Recherche Opérationnelle
%D 2018
%P 429-438
%V 52
%N 2
%I EDP-Sciences
%U http://archive.numdam.org/articles/10.1051/ro/2017037/
%R 10.1051/ro/2017037
%G en
%F RO_2018__52_2_429_0
Brahimi, Nassim; Salhi, Abdellah; Ourbih-Tari, Megdouda. Convergence analysis of the plant propagation algorithm for continuous global optimization. RAIRO - Operations Research - Recherche Opérationnelle, Tome 52 (2018) no. 2, pp. 429-438. doi : 10.1051/ro/2017037. http://archive.numdam.org/articles/10.1051/ro/2017037/

[1] E.H.L. Aarts, J. Korst and P.J.M. Van Laarhoven, Simulated Annealing. In Local Search in Combinatorial Optimization. edited by E.H.L. Aarts and J.K. Lenstra. Wiley (1997) 91–120 | MR | Zbl

[2] A. Agapie, M. Agapie, G. Rudolph, G. Zbaganu, Convergence of evolutionary algorithms on the n-dimensional continuous space. Cybernetics. IEEE Trans. 43 (2013) 1462–1472

[3] F. Bergh and A. Engelbrecht, A convergence proof for the particle swarm optimiser. Fundamenta Informaticae 105 (2010) 341–374 | DOI | MR | Zbl

[4] A. Bienvenue and O. Francois, Global convergence for evolution strategies in spherical problems: some simple proofs and difficulties. Theoretical Comput. Sci. 306 (2003) 269–289 | DOI | MR | Zbl

[5] N. Brahimi, A. Salhi and M. Ourbih-Tari, Drift Analysis of Ant Colony Optimization of Stochastic Linear Pseudo-Boolean Functions. Operat. Res. Lett. 45 (2017) 342–347 | DOI | MR | Zbl

[6] J. Brownlee, Clever Algorithms: Nature-Inspired Programming Recipes (2011)

[7] M. Clerc, Particle Swarm Optimization. In Vol. 93. John Wiley and Sons (2010)

[8] E. Cuevas and M. Cienfuegos, A new algorithm inspired in the behavior of the social-spider for constrained optimization. Expert Syst. Appl. 41 (2014) 412–425 | DOI

[9] R. Eberhart, J. Kennedy, A new optimizer using particle swarm theory. In Proc. of the 6th Inter. Sympos. Micro Machine and Human Scie. (MHS’95), IEEE, Nagoya, Japan (1995) 39–43 | DOI

[10] A.H. Gandomi, X.-S. Yang and A.H. Alavi, Mixed variable structural optimization using Firefly Algorithm. Comput. Structures 89 (2011) 2325–2336 | DOI

[11] W. Gutjahr, A graph-based ant system and its convergence. Future Generation Comput. Syst. 16 (2000) 873–888 | DOI

[12] W. Gutjahr, ACO algorithms with guaranteed convergence to the optimal solutions. Information Processing Lett. 82 (2002) 145–53 | DOI | MR | Zbl

[13] P. Hansen and N. Mladenovic, Variable neighborhood search: Principles and applications. Eur. J. Operat. Res. 130 (2001) 449–467 | DOI | MR | Zbl

[14] J.H. Holland, Adaptation in Natural and Artificial Systems, University of Michigan Press. Ann. Arbor, MI (1974) | MR | Zbl

[15] D. Karaboga, An idea based on honey bee swarm for numerical optimization. Tech. Rep. (TR06), Erciyes University Press, Kayseri, Turkey (2005)

[16] D. Karaboga and B. Basturk, On the performance of artificial bee colony (ABC) algorithm. Appl. Soft Comput. J. 8 (2008) 687–697 | DOI

[17] M. Kazemian, Y. Ramezani, C. Lucas and B. Moshiri, Swarm clustering based on flowers pollination by artificial bees. Studies Comput. Intell. 34 (2006) 191–202 | DOI

[18] S. Kirkpatrick, C.D. Gelatt and M.P. Vecchi, Optimization by Simulated Annealing. Science 220 (1983) 671–680 | DOI | MR | Zbl

[19] N. Metropolis, A.W. Rosenbluth, M. Rosenbluth, A.H. Teller and E. Teller, Equation of State Calculations by Fast Computing Machines. J. Chem. Phys. 21 (1953) 1087–1092 | DOI | Zbl

[20] A. Salhi and E. Fraga, Nature-inspired optimisation approaches and the new plant propagation algorithm. In Proc. of the International Conference on Numerical Analysis and Optimization ICeMATH ’11 (2011) K2-1–K2-8, Yogyakarta, Indonesia.

[21] A. Salhi, L.G. Proll, D. Rios Insua and J.I. Martin, Experiences with stochastic algorithms for a class of constrained global optimisation problems. Recherche Opér. 34 (2000) 183–97 | Numdam | MR | Zbl

[22] A. Salhi, J.A. Vázquez-Rodríguez and Q. Zhang, An estimation of distribution algorithm with guided mutation for a complex flow shop scheduling problem. In Proc. of 9th Ann. Genetic and Evolutionary Comput. Conf. (GECCO’07), edited by D. Thierens. ACM (2007) 570–576 | DOI

[23] M. Sulaiman, A. Salhi, E.S. Fraga and W.K. Mashwani, M.M. Rashidi, A Novel Plant Propagation Algorithm: Modifications And Implementation. Science International-Lahore 28 (2015) 201–209

[24] M. Sulaiman and A. Salhi, A Seed-based Plant Propagation Algorithm: The feeding Station Model. Scientific World J. 2015 (2015) 1–16 | DOI

[25] M. Sulaiman, A. Salhi, B. Selamoglu and O. Kirikchi, A plant propagation algorithm for constrained engineering optimisation problems. Math. Problems Eng. 2014 (2014) 1–10 | DOI

[26] R. Yang, Convergence of the simulated annealing algorithm for continuous global optimization. J. Optimiz. Theory Appl. 104 (2000) 691–716 | DOI | MR | Zbl

[27] X.-S. Yang, Firefly algorithm, stochastic test functions and design optimisation. Inter. J. Bio-Inspired Comput. 2 (2010) 78–84 | DOI

[28] X.-S. Yang, Nature-Inspired Metaheuristic Algorithms. Luniver Press (2011)

[29] X.-S. Yang, Flower pollination algorithm for global optimization. In Unconventional Computation and Natural Computation. Springer (2012) 240–249 | DOI | Zbl

Cité par Sources :