A firefly algorithm with chaotic diversity control

XU Huali; SU Shoubao; YAN Renrong; MA Yan

doi:10.3969/j.issn.0253-2778.2014.07.011

JUSTC > 2014 > 44(7): 612-617. > DOI: 10.3969/j.issn.0253-2778.2014.07.011

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A firefly algorithm with chaotic diversity control

1.
School of Information and Engineering, West AnHui University, Luan 237012, China
2.
Nanjing Institute of Software, Chinese Academy of Sciences, Nanajing 211169, China
3.
School of Computers, Jinling Institute of Technology, Nanjing 211169, China

Cite this: JUSTC, 2014, 44(7): 612-617

https://doi.org/10.3969/j.issn.0253-2778.2014.07.011

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Received Date: March 20, 2014
Revised Date: April 14, 2014
Accepted Date: April 14, 2014
Published Date: July 29, 2014

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Abstract

Abstract

To overcome the disadvantage of premature convergence in the firefly algorithm, a firefly algorithm based on chaos diversity control (CDFA) was proposed. Applying chaotic mapping, CDFA achieved an initial firefly population that is high quality and uniformly distributed; it then disturbed some individuals with low fitness values by chaotic mapping in the process of the search so as to keep the groups activity and reduce the possibility of falling into local optimum; meanwhile, in order to increase the diversity of the population, the proposed algorithm used the physical reflection theory to control the position of the firefly outside the borders. Experimental results of bench mark functions show that CDFA can effectively improve the ability of the global search and local exploitation and has a better optimization precision and convergence rate than the basic FA.

Abstract

To overcome the disadvantage of premature convergence in the firefly algorithm, a firefly algorithm based on chaos diversity control (CDFA) was proposed. Applying chaotic mapping, CDFA achieved an initial firefly population that is high quality and uniformly distributed; it then disturbed some individuals with low fitness values by chaotic mapping in the process of the search so as to keep the groups activity and reduce the possibility of falling into local optimum; meanwhile, in order to increase the diversity of the population, the proposed algorithm used the physical reflection theory to control the position of the firefly outside the borders. Experimental results of bench mark functions show that CDFA can effectively improve the ability of the global search and local exploitation and has a better optimization precision and convergence rate than the basic FA.

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References (22)

References

[1]	Yang X S. Nature-Inspired Metaheuristic Algorithms [M]. UK: Luniver Press, 2008.
[2]	Yang X S. Firefly algorithms for multimodal optimization[C]// Proceedings of the 5th International Conference on Stochastic Algorithms: Foundations and Applications. Sapporo, Japan: Springer, 2009: 169-178.
[3]	Senthilnath J, Omkar S N, Mani V. Clustering using firefly algorithm: Performance study[J]. Swarm and Evolutionary Computation, 2011, 1(3): 164-171.
[4]	Horng M H, Jiang T W. The codebook design of image vector quantization based on the firefly algorithm[C]// Proceedings of the 2nd International Conference on Computational Collective Intelligence: Technologies and Applications. Kaohsiung, Taiwan, China: Springer, 2010: 438-447.
[5]	Horng M H. Vector quantization using the firefly algorithm for image compression[J]. Expert Systems with Applications, 2012, 39(1): 1 078-1 091.
[6]	Hnig U. A firefly algorithm-based approach for scheduling task graphs in homogenous systems[C]// Proceedings of the International Conference on Informatics. Anaheim, USA: ACTA Press, 2010: 256-263.
[7]	Jati G K, Suyanto. Evolutionary discrete firefly algorithm for travelling salesman problem[C]// Proceedings of the 2nd International Conference on Adaptive and Intelligent Systems. Klagenfurt, Austria: Springer, 2011, 6943: 393-403.
[8]	Basu B, Mahanti G K. Firefly and artificial bees colony algorithm for synthesis of scanned and broadside linear array antenna[J]. Progress in Electromagnetics Research B, 2011, 32: 169-190.
[9]	Chatterjee A, Mahanti G K, Chatterjee A. Design of a fully digital controlled reconfigurable switched beam concentric ring array antenna using firefly and particle swarm optimization algorithm[J]. Progress in Electromagnetics Research B, 2012, 36: 113-131.
[10]	Farahani S M, Nasiri B, Meybodi M R. A multiswarm based firefly algorithm in dynamic environments[C]// Proceedings of the 3rd International Conference on Signal Processing Systems. Yantai, China: IEEE Press, 2011: 68-72.
[11]	Abshouri A A, Meybodi M R, Bakhtiary A. New firefly algorithm based on multi swarm and learning automata in dynamic environments [C] // Proceedings of the 3rd International Conference on Signal Processing Systems. Yantai, China: IEEE Press, 2011: 73-77.
[12]	Zhang Y D, Wu L N. A novel method for rigid image registration based on firefly algorithm [J]. International Journal of Research and Reviews in Soft and Intelligent Computing, 2012, 2(2): 141-146.
[13]	Luthra J, Pal S K. A hybrid firefly algorithm using genetic operators for the cryptanalysis of a monoalphabetic substitution cipher[C]// World Congress on Information and Communication Technologies. Mumbai, India: IEEE Press, 2011: 202-206.
[14]	Fister Jr I, Yang X S, Fister I, et al. Memetic firefly algorithm for combinatorial optimization[C]// Proceedings of the 5th International Conference on Bioinspired Optimization Methods and their Applications. Ljubljana, Slovenia: IEEE Press, 2012: 75-86.
[15]	Aruchamy R, Vasantha K D. A comparative performance study on hybrid swarm model for micro array data[J]. International Journal of Computer Applications, 2011, 30(6): 10-14.
[16]	Abdullah A, Deris S, Mohamad M S, et al. A new hybrid firefly algorithm for complex and nonlinear problem[C]// 9th International Conference on Distributed Computing and Artificial Intelligence. Springer, 2012: 673-680.
[17]	冯艳红,刘建芹,贺毅朝.基于混沌理论的动态种群萤火虫算法[J].计算机应用, 2013, 33(3): 796-799.
[18]	Lu H J, Zhang H M, Ma L H. A new optimization algorithm based on chaos[J]. Zhejiang University Science A, 2006, 7(4): 539-542.
[19]	Tavazoei M S, HaerI M. An optimization algorithm based on chaotic behavior and fractal nature[J]. Journal of Computational and Applied Mathematics, 2007, 206(2): 1 070-1 081.
[20]	Choi C, Lee J J. Chaotic local serch algorithm[J]. Artificial Life and Robotics, 1998, 2(1): 41-47.
[21]	徐刚,杨玉群,刘斌斌,等.一种基于多样性策略的粒子群算法[J].南昌大学学报, 2013, 37(1): 17-21.
[22]	赫然,王永吉,王青,等.一种改进的自适应逃逸微粒群算法及实验分析[J]. 软件学报, 2005, 16(12): 2 036-2 044.

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References

[1]	Yang X S. Nature-Inspired Metaheuristic Algorithms [M]. UK: Luniver Press, 2008.
[2]	Yang X S. Firefly algorithms for multimodal optimization[C]// Proceedings of the 5th International Conference on Stochastic Algorithms: Foundations and Applications. Sapporo, Japan: Springer, 2009: 169-178.
[3]	Senthilnath J, Omkar S N, Mani V. Clustering using firefly algorithm: Performance study[J]. Swarm and Evolutionary Computation, 2011, 1(3): 164-171.
[4]	Horng M H, Jiang T W. The codebook design of image vector quantization based on the firefly algorithm[C]// Proceedings of the 2nd International Conference on Computational Collective Intelligence: Technologies and Applications. Kaohsiung, Taiwan, China: Springer, 2010: 438-447.
[5]	Horng M H. Vector quantization using the firefly algorithm for image compression[J]. Expert Systems with Applications, 2012, 39(1): 1 078-1 091.
[6]	Hnig U. A firefly algorithm-based approach for scheduling task graphs in homogenous systems[C]// Proceedings of the International Conference on Informatics. Anaheim, USA: ACTA Press, 2010: 256-263.
[7]	Jati G K, Suyanto. Evolutionary discrete firefly algorithm for travelling salesman problem[C]// Proceedings of the 2nd International Conference on Adaptive and Intelligent Systems. Klagenfurt, Austria: Springer, 2011, 6943: 393-403.
[8]	Basu B, Mahanti G K. Firefly and artificial bees colony algorithm for synthesis of scanned and broadside linear array antenna[J]. Progress in Electromagnetics Research B, 2011, 32: 169-190.
[9]	Chatterjee A, Mahanti G K, Chatterjee A. Design of a fully digital controlled reconfigurable switched beam concentric ring array antenna using firefly and particle swarm optimization algorithm[J]. Progress in Electromagnetics Research B, 2012, 36: 113-131.
[10]	Farahani S M, Nasiri B, Meybodi M R. A multiswarm based firefly algorithm in dynamic environments[C]// Proceedings of the 3rd International Conference on Signal Processing Systems. Yantai, China: IEEE Press, 2011: 68-72.
[11]	Abshouri A A, Meybodi M R, Bakhtiary A. New firefly algorithm based on multi swarm and learning automata in dynamic environments [C] // Proceedings of the 3rd International Conference on Signal Processing Systems. Yantai, China: IEEE Press, 2011: 73-77.
[12]	Zhang Y D, Wu L N. A novel method for rigid image registration based on firefly algorithm [J]. International Journal of Research and Reviews in Soft and Intelligent Computing, 2012, 2(2): 141-146.
[13]	Luthra J, Pal S K. A hybrid firefly algorithm using genetic operators for the cryptanalysis of a monoalphabetic substitution cipher[C]// World Congress on Information and Communication Technologies. Mumbai, India: IEEE Press, 2011: 202-206.
[14]	Fister Jr I, Yang X S, Fister I, et al. Memetic firefly algorithm for combinatorial optimization[C]// Proceedings of the 5th International Conference on Bioinspired Optimization Methods and their Applications. Ljubljana, Slovenia: IEEE Press, 2012: 75-86.
[15]	Aruchamy R, Vasantha K D. A comparative performance study on hybrid swarm model for micro array data[J]. International Journal of Computer Applications, 2011, 30(6): 10-14.
[16]	Abdullah A, Deris S, Mohamad M S, et al. A new hybrid firefly algorithm for complex and nonlinear problem[C]// 9th International Conference on Distributed Computing and Artificial Intelligence. Springer, 2012: 673-680.
[17]	冯艳红,刘建芹,贺毅朝.基于混沌理论的动态种群萤火虫算法[J].计算机应用, 2013, 33(3): 796-799.
[18]	Lu H J, Zhang H M, Ma L H. A new optimization algorithm based on chaos[J]. Zhejiang University Science A, 2006, 7(4): 539-542.
[19]	Tavazoei M S, HaerI M. An optimization algorithm based on chaotic behavior and fractal nature[J]. Journal of Computational and Applied Mathematics, 2007, 206(2): 1 070-1 081.
[20]	Choi C, Lee J J. Chaotic local serch algorithm[J]. Artificial Life and Robotics, 1998, 2(1): 41-47.
[21]	徐刚,杨玉群,刘斌斌,等.一种基于多样性策略的粒子群算法[J].南昌大学学报, 2013, 37(1): 17-21.
[22]	赫然,王永吉,王青,等.一种改进的自适应逃逸微粒群算法及实验分析[J]. 软件学报, 2005, 16(12): 2 036-2 044.

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