An Improved Firefly Algorithm Based on Local Search Method for Solving Global Optimization Problems

R. M. Rizk-Allah

doi:doi:10.11648/j.ijmfs.20160206.11

| Peer-Reviewed

An Improved Firefly Algorithm Based on Local Search Method for Solving Global Optimization Problems

R. M. Rizk-Allah

Published in International Journal of Management and Fuzzy Systems (Volume 2, Issue 6)

Received: 9 December 2016 Accepted: 20 December 2016 Published: 1 March 2017

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

Abstract

This paper proposes an improved firefly algorithm (IFA)based on local search method for solving globaloptimization problems. The main feature of the proposed algorithm is to improve the solutions quality generated from the fireflies by embedding the local search method. Moreover, the new solutions are generated based on the movement formula of the fireflies that is modified by exponential formula. The exponential formula reduces the randomization parameter so that it decreases gradually as the optimum is approaching. In addition, local search method (LSM) is introduced to improve the solution quality. Finally, the proposed algorithm is tested on several benchmark problems from the usual literature and the numerical results have demonstrated the superiority of the proposed algorithm in finding the global optimal solution.

Published in	International Journal of Management and Fuzzy Systems (Volume 2, Issue 6)
DOI	10.11648/j.ijmfs.20160206.11
Page(s)	51-57
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Firefly Algorithm, Local Search Method, Global Optimization

References

[1]	J. H. Holland, Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor, MI, 1975.
[2]	R. Storn, Differential evolution design of an IIR-filter, in: IEEE International Conference on Evolutionary Computation, Nagoya, 1996, pp. 268–273.
[3]	J. Kennedy, R. C. Eberhart, Particles warm optimization, in: Proceedings of IEEE International Conference on Neural Networks, 1995, pp. 1942–1948.
[4]	M. Dorigo, T. Stützle, Ant Colony Optimization, MIT Press, London, 2004.
[5]	S. Kirkpatrick, C. Gelatt, M. Vecchi, Optimization by simulated annealing, Science 220, 1983, pp. 671–680.
[6]	M. H. Mashinchi, A. O. Mehmet, P. Witold, Hybrid optimization with improved tabu search, Applied Soft Computing 11, 2011, pp. 1993–2006.
[7]	A. Y. Qing, Dynamic differential evolution strategy and applications in electromagnetic inverses catering problems, IEEE Transactions on Geo science and Remote Sensing 44 (1), 2006, pp. 116–125.
[8]	J. H. V. Sickel, K. Y. Lee, J. S. Heo, Differential Evolution and its Applications to Power Plant Control, in: International Conference on Intelligent Systems Applications to Power Systems, ISAP, 2007, pp. 1–6.
[9]	Y. Marinakis, M. Marinaki, G. Dounias, Particles warm optimization for pap-smear diagnosis, Expert Systems with Applications 35 (4), 2008, pp. 1645–1656.
[10]	M. Serrurier, H. Prade, Improving inductive logic programming by using simulated annealing, Information Sciences 178 (6), 2008, pp. 1423–1441.
[11]	M. Dorigo, Learning and Nature Algorithm (in Italian). Ph. D. Dissertation, Dipartimento di Electtonica, Politecnico di Milano, Italy, 1992.
[12]	L. Chen, J. Shen, L. Qin, H J. Chen, An improved ant colony algorithm in continuous optimization. Journal of Systems Science and Systems Engineering, 12 (2), 2003, pp. 224-235.
[13]	J. Dreo, P. Siarry, An ant colony algorithm aimed at dynamic continuous optimization. Appl. Math. Comput., 181, 2006, pp. 457-467.
[14]	A. A. Mousa, Waiel F. Abd El-Wahed, R. M. Rizk-Allah, A hybrid ant colony optimization approach based local search scheme for multiobjective design optimizations, Electric Power Systems Research, 81, 2011, pp. 1014–1023.
[15]	C. Blum, Beam-ACO-Hybridizing ant colony optimization with beam search: An application to open shop scheduling. Computers & Operations Res32 (6), 2005, pp. 1565–1591.
[16]	Z. Xiaoxia, T. Lixin, A new hybrid ant colony optimization algorithm for the vehicle routing problem, Pattern Recognition Letters 30, 2009, pp. 848–855.
[17]	P. Hadi, T. M. Reza, Solving a multi-objective open shop scheduling problem by a novel hybrid ant colony optimization, Expert Systems with Applications 38, 2011, pp. 2817–2822.
[18]	Y. Jingan, Z. Yanbin, An improved ant colony optimization algorithm for solving a complex combinatorial optimization problem, Applied Soft Computing 10, 2010, pp. 653–660.
[19]	P. S. Shelokar, P. Siarry, V. K. Jayaraman, B. D. Kulkarni, Particle swarm and ant colony algorithms hybridized for improved continuous optimization. Applied Mathematics and Computation 188, 2007, pp. 129-142.
[20]	C. Shyi-Ming, C. Chih-Yao, Parallelized genetic ant colony systems for solving the traveling salesman problem, Expert Systems with Applications 38, 2011, pp. 3873–3883.
[21]	X. S. Yang.: Nature-Inspired Metaheuristic Algorithms. Luniver Press (2008).
[22]	M. S. Bazaraa, C. M. Shetly, Nonlinear Programming Theory and Algorithms, Wiley, NewYork, 1979.
[23]	K. Hamzacebi, F. Kutay, continuous functions minimization by dynamic randomsearch technique, Applied Mathematical Modelling 31, 2007, pp. 2189-2198.

Cite This Article

Plain Text BibTeX RIS

APA Style

R. M. Rizk-Allah. (2017). An Improved Firefly Algorithm Based on Local Search Method for Solving Global Optimization Problems. International Journal of Management and Fuzzy Systems, 2(6), 51-57. https://doi.org/10.11648/j.ijmfs.20160206.11

Copy | Download

ACS Style

R. M. Rizk-Allah. An Improved Firefly Algorithm Based on Local Search Method for Solving Global Optimization Problems. Int. J. Manag. Fuzzy Syst. 2017, 2(6), 51-57. doi: 10.11648/j.ijmfs.20160206.11

Copy | Download

AMA Style

R. M. Rizk-Allah. An Improved Firefly Algorithm Based on Local Search Method for Solving Global Optimization Problems. Int J Manag Fuzzy Syst. 2017;2(6):51-57. doi: 10.11648/j.ijmfs.20160206.11

Copy | Download

@article{10.11648/j.ijmfs.20160206.11,
  author = {R. M. Rizk-Allah},
  title = {An Improved Firefly Algorithm Based on Local Search Method for Solving Global Optimization Problems},
  journal = {International Journal of Management and Fuzzy Systems},
  volume = {2},
  number = {6},
  pages = {51-57},
  doi = {10.11648/j.ijmfs.20160206.11},
  url = {https://doi.org/10.11648/j.ijmfs.20160206.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijmfs.20160206.11},
  abstract = {This paper proposes an improved firefly algorithm (IFA)based on local search method for solving globaloptimization problems. The main feature of the proposed algorithm is to improve the solutions quality generated from the fireflies by embedding the local search method. Moreover, the new solutions are generated based on the movement formula of the fireflies that is modified by exponential formula. The exponential formula reduces the randomization parameter so that it decreases gradually as the optimum is approaching. In addition, local search method (LSM) is introduced to improve the solution quality. Finally, the proposed algorithm is tested on several benchmark problems from the usual literature and the numerical results have demonstrated the superiority of the proposed algorithm in finding the global optimal solution.},
 year = {2017}
}

Copy | Download

TY  - JOUR
T1  - An Improved Firefly Algorithm Based on Local Search Method for Solving Global Optimization Problems
AU  - R. M. Rizk-Allah
Y1  - 2017/03/01
PY  - 2017
N1  - https://doi.org/10.11648/j.ijmfs.20160206.11
DO  - 10.11648/j.ijmfs.20160206.11
T2  - International Journal of Management and Fuzzy Systems
JF  - International Journal of Management and Fuzzy Systems
JO  - International Journal of Management and Fuzzy Systems
SP  - 51
EP  - 57
PB  - Science Publishing Group
SN  - 2575-4947
UR  - https://doi.org/10.11648/j.ijmfs.20160206.11
AB  - This paper proposes an improved firefly algorithm (IFA)based on local search method for solving globaloptimization problems. The main feature of the proposed algorithm is to improve the solutions quality generated from the fireflies by embedding the local search method. Moreover, the new solutions are generated based on the movement formula of the fireflies that is modified by exponential formula. The exponential formula reduces the randomization parameter so that it decreases gradually as the optimum is approaching. In addition, local search method (LSM) is introduced to improve the solution quality. Finally, the proposed algorithm is tested on several benchmark problems from the usual literature and the numerical results have demonstrated the superiority of the proposed algorithm in finding the global optimal solution.
VL  - 2
IS  - 6
ER  -

Copy | Download

Author Information

R. M. Rizk-Allah

Department of Basic Engineering Science, Faculty of Engineering, Minoufia University, Shebin El-Kom, Egypt

Download PDF

Sections

Plain Text BibTeX RIS

APA Style

R. M. Rizk-Allah. (2017). An Improved Firefly Algorithm Based on Local Search Method for Solving Global Optimization Problems. International Journal of Management and Fuzzy Systems, 2(6), 51-57. https://doi.org/10.11648/j.ijmfs.20160206.11

Copy | Download

ACS Style

R. M. Rizk-Allah. An Improved Firefly Algorithm Based on Local Search Method for Solving Global Optimization Problems. Int. J. Manag. Fuzzy Syst. 2017, 2(6), 51-57. doi: 10.11648/j.ijmfs.20160206.11

Copy | Download

AMA Style

R. M. Rizk-Allah. An Improved Firefly Algorithm Based on Local Search Method for Solving Global Optimization Problems. Int J Manag Fuzzy Syst. 2017;2(6):51-57. doi: 10.11648/j.ijmfs.20160206.11

Copy | Download

@article{10.11648/j.ijmfs.20160206.11,
  author = {R. M. Rizk-Allah},
  title = {An Improved Firefly Algorithm Based on Local Search Method for Solving Global Optimization Problems},
  journal = {International Journal of Management and Fuzzy Systems},
  volume = {2},
  number = {6},
  pages = {51-57},
  doi = {10.11648/j.ijmfs.20160206.11},
  url = {https://doi.org/10.11648/j.ijmfs.20160206.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijmfs.20160206.11},
  abstract = {This paper proposes an improved firefly algorithm (IFA)based on local search method for solving globaloptimization problems. The main feature of the proposed algorithm is to improve the solutions quality generated from the fireflies by embedding the local search method. Moreover, the new solutions are generated based on the movement formula of the fireflies that is modified by exponential formula. The exponential formula reduces the randomization parameter so that it decreases gradually as the optimum is approaching. In addition, local search method (LSM) is introduced to improve the solution quality. Finally, the proposed algorithm is tested on several benchmark problems from the usual literature and the numerical results have demonstrated the superiority of the proposed algorithm in finding the global optimal solution.},
 year = {2017}
}

Copy | Download

TY  - JOUR
T1  - An Improved Firefly Algorithm Based on Local Search Method for Solving Global Optimization Problems
AU  - R. M. Rizk-Allah
Y1  - 2017/03/01
PY  - 2017
N1  - https://doi.org/10.11648/j.ijmfs.20160206.11
DO  - 10.11648/j.ijmfs.20160206.11
T2  - International Journal of Management and Fuzzy Systems
JF  - International Journal of Management and Fuzzy Systems
JO  - International Journal of Management and Fuzzy Systems
SP  - 51
EP  - 57
PB  - Science Publishing Group
SN  - 2575-4947
UR  - https://doi.org/10.11648/j.ijmfs.20160206.11
AB  - This paper proposes an improved firefly algorithm (IFA)based on local search method for solving globaloptimization problems. The main feature of the proposed algorithm is to improve the solutions quality generated from the fireflies by embedding the local search method. Moreover, the new solutions are generated based on the movement formula of the fireflies that is modified by exponential formula. The exponential formula reduces the randomization parameter so that it decreases gradually as the optimum is approaching. In addition, local search method (LSM) is introduced to improve the solution quality. Finally, the proposed algorithm is tested on several benchmark problems from the usual literature and the numerical results have demonstrated the superiority of the proposed algorithm in finding the global optimal solution.
VL  - 2
IS  - 6
ER  -

Copy | Download