Automatic differentiation based for particle swarm optimization Steepest descent direction

(1) Aris Thobirin Mail (University of Ahmad Dahlan)
(2) * Iwan Tri Riyadi Yanto Mail (University of Ahmad Dahlan, Indonesia)
*corresponding author


Particle swam optimization (PSO) is one of the most effective optimization methods to find the global optimum point. In other hand, the descent direction (DD) is the gradient based method that has the local search capability. The combination of both methods is promising and interesting to get the method with effective global search capability and efficient local search capability. However, In many application, it is difficult or impossible to obtain the gradient exactly of an objective function. In this paper, we propose Automatic differentiation (AD) based for PSODD. we compare our methods on benchmark function. The results shown that the combination methods give us a powerful tool to find the solution.


Particle swam optimization; Gradient; Descent direction;AD



Article metrics

Abstract views : 2858 | PDF views : 416




Full Text



J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of ICNN’95 - International Conference on Neural Networks, vol. 4, pp. 1942–1948.

H. Shen, X. Peng, J. Wang, and Z. Hu, “A Mountain Clustering Based on Improved PSO Algorithm,” in Advances in Natural Computation SE - 58, vol. 3612, L. Wang, K. Chen, and Y. Ong, Eds. Springer Berlin Heidelberg, 2005, pp. 477–481.

Q. Li, Z. Shi, J. Shi, and Z. Shi, “Swarm Intelligence Clustering Algorithm Based on Attractor,” Intelligence, no. 60435010, pp. 496–504, 2005.

R. Eberhart and J. Kennedy, “A new optimizer using particle swarm theory,” MHS’95. Proc. Sixth Int. Symp. Micro Mach. Hum. Sci., pp. 39–43, 1995.

E. Cuevas, A. Echavarría, and M. Ramírez-Ortegón, “An optimization algorithm inspired by the States of Matter that improves the balance between exploration and exploitation,” Appl. Intell., vol. 40, no. 2, pp. 256–272, 2014.

B. Ostadmohammadi Arani, P. Mirzabeygi, and M. Shariat Panahi, “An improved PSO algorithm with a territorial diversity-preserving scheme and enhanced exploration–exploitation balance,” Swarm Evol. Comput., vol. 11, no. 0, pp. 1–15, Aug. 2013.

S. F. Adra and P. J. Fleming, “Diversity Management in Evolutionary Many-Objective Optimization,” IEEE Trans. Evol. Comput., vol. 15, no. 2, pp. 183–195, Apr. 2011.

M. Črepinšek, S.-H. Liu, and M. Mernik, “Exploration and exploitation in evolutionary algorithms,” ACM Comput. Surv., vol. 45, no. 3, pp. 1–33, Jun. 2013.

I. Paenke and J. Branke, “Balancing Population- and Individual-Level Adaptation in Changing Environments,” Adapt. Behav., vol. 17, no. 2, pp. 153–174, Mar. 2009.

M. A. Bhatti, Practical Optimization Methods. New York, NY: Springer New York, 2000.

L. M. Graña Drummond and B. F. Svaiter, “A steepest descent method for vector optimization,” J. Comput. Appl. Math., vol. 175, no. 2, pp. 395–414, 2005.

R. Burachik, L. M. Graña Drummond, a. N. Iusem, and B. F. Svaiter, “Full convergence of the steepest descent method with inexact line searches,” Optimization, vol. 32, no. 2, pp. 137–146, 1995.

L. B. Rall, Ed., Automatic Differentiation: Techniques and Applications, vol. 120. Berlin, Heidelberg: Springer Berlin Heidelberg, 1981.

M. Bartholomew-biggs, S. Brown, B. Christianson, and L. Dixon, “Automatic di erentiation of algorithms,” J. Comput. Appl. Math., vol. 124, pp. 171–190, 2000.

J. Tang and L. Dong, “A new descent algorithm with curve search rule for unconstrained minimization,” in 2010 Second International Conference on Computational Intelligence and Natural Computing, 2010, vol. 1, pp. 89–92.

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

International Journal of Advances in Intelligent Informatics
ISSN 2442-6571  (print) | 2548-3161 (online)
Organized by UAD and ASCEE Computer Society
Published by Universitas Ahmad Dahlan
E: (paper handling issues) (publication issues)

View IJAIN Stats

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0