Abstract
This chapter presents the motivation of when, why, and how the fireworks algorithm (FWA), as a novel swarm intelligence optimization algorithm, came out. After a concise review on swarm intelligence domain, a brief introduction to FWA is presented with primary focuses on four aspects of theoretical analysis, algorithm study, problem solving, and applications. The characteristics and advantages of FWA are also described. Finally, overviews of FWA research are detailed with completed reference citations.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
M.T. Hagan, H.B. Demuth, M.H. Beale et al., Neural Network Design (Pws Pub, Boston, 1996)
G.C. Ruan, Y. Tan, A three-layer back-propagation neural network for spam detection using artificial immune concentration. Softcomputing 14, 139–150 (2010)
X. Huang, Y. Tan, X.G. He, An intelligent multi-feature statistical approach for discrimination of driving conditions of hybrid electric vehicle. IEEE Trans. Intell. Transp. Syst. 12(2), 453–456 (2011)
Y. Tan, C. Deng, Solving for a quadratic programming with a quadratic constraint based on a neural network frame. Neurocomputing 30, 117–128 (2000)
Y. Tan et al., Neural network design approach of cosine-modulated FIR filter bank and compactly supported wavelets with almost PR property. Signal Process. 69(1), 29–48 (1998)
Y. Tan, Z.K. Liu, On matrix eigendecomposition by neural networks. (Neural Netw. World) International Journal on Neural and Mass-Parallel Computing and Information Systems 8(3), 337–352 (1998)
G.J. Klir, B. Yuan, Fuzzy Sets and Fuzzy Logic, vol. 4 (Prentice Hall, NewD Jersey, 1995)
A.E. Eiben, J.E. Smith, Introduction to Evolutionary Computing (springer, Berlin, 2003)
Y. Tan, J. Wang, Nonlinear blind separation using higher-order statistics and a genetic algorithm. IEEE Trans. Evol. Comput. 5(6), 600–612 (2001)
J. Zhang, Y. Tan, L. Ni, C. Xie, Z. Tang, AMT-PSO: an adaptive magnification transformation based particle swarm optimizer. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. E94-D(4): 786–797 (2011)
Y. Tan, J. Wang, A support vector network with hybrid kernel and minimal Vapnik-Chervonenkis dimension. IEEE Trans. Knowl. Data Eng. 26(2), 385–395 (2004)
H.-O. Peitgen, H. Jrgens, D. Saupe, Chaos and Fractals: New Frontiers of Science (Springer, Berlin, 2004)
P.J.M. Van Laarhoven, E.H.L. Aarts, Simulated Annealing (Springer, Berlin, 1987)
F. Glover, M. Laguna, Tabu Search (Springer, 1999)
Y. Tan, S. Zheng, Research progress on swarm intelligence optimization algorithms. Commun. Chin. Autom. Soc. 34(3), (2013)
M. Dorigo, M. Birattari, T. Stutzle, Ant colony optimization. IEEE Comput. Intell. Mag. 1(4), 28–39 (2006)
J. Kennedy, R. Eberhart et al., Particle swarm optimization, in Proceedings of IEEE International Conference on Neural Networks, vol. 4(2) (Perth, Australia, 1995), pp. 1942–1948
C.J.A Bastos Filho, F.B. de Lima Neto, A.J.C.C. Lins, A.I.S. Nascimento, M.P. Lima, Fish school search, in Nature-Inspired Algorithms for Optimisation (Springer, Berlin, 2009) pp. 261–277
S. Ukasik, S. Ak, Firefly algorithm for continuous constrained optimization tasks, in Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems (Springer, Heidelberg, 2009), pp. 97–106
X.-S. Yang, Firefly algorithms for multimodal optimization, in Stochastic Algorithms: Foundations and Applications (Springer, Berlin, 2009), pp. 169–178
X.-S. Yang, S. Deb, Cuckoo search via Lvy flights, in 2009 World Congress on IEEE Nature & Biologically Inspired Computing (NaBIC) (IEEE, 2009), pp. 210–214
X.-S. Yang, A new metaheuristic bat-inspired algorithm, in Nature Inspired Cooperative Strategies for Optimization (NICSO) (Springer, Berlin, 2010), pp. 65–74
D. Karaboga, An idea based on honey bee swarm for numerical optimization. Technical report-tr06, Erciyes University, Engineering Faculty, Computer Engineering Department, (2005)
D. Karaboga, B. Basturk, A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J. Glob. Optim. 39(3), 459–471 (2007)
D. Karaboga, B. Basturk, On the performance of artificial bee colony (ABC) algorithm. Appl. Soft Comput. 8(1), 687–697 (2008)
C.R. Blomeke, S.J. Elliott, T.M. Walter, Bacterial survivability and transferability on biometric devices, in 2007 41st Annual IEEE International Carnahan Conference on Security Technology (IEEE 2007), pp. 80–84
Y. Tan, Y. Zhu, Fireworks algorithm for optimization, in Advances in Swarm Intelligence (Springer, Berlin, 2010), pp. 355–364
H. Shah-Hosseini, The intelligent water drops algorithm: a nature-inspired swarm-based optimization algorithm. Int. J. Bio-Inspir. Comput. 1(1), 71–79 (2009)
Y. Shi, Brain storm optimization algorithm, in Advances in Swarm Intelligence (Springer, Berlin, 2011), pp. 303–309
N.M.H. Tayarani, M.R. Akbarzadeh-T, Magnetic optimization algorithms a new synthesis, in 2008 IEEE World Congress on Computational Intelligence Evolutionary Computation (CEC) (IEEE, 2008), pp. 2659–2664
D.H. Wolpert, W.G. Macready, No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1(1), 67–322 (1997)
D.H. Wolpert, W.G. Macready, Coevolutionary free lunches. IEEE Trans. Evol. Comput. 9(6), 721–735 (2005)
J. Liu, S. Zheng, Y. Tan, Analysis on global convergence and timecomplexity of fireworks algorithm, in IEEE Congress on Evolutionary Computation (CEC’2014) (Beijing, China, 2014), pp. 3207–3213
K. Ding, S. Zheng, Y. Tan. A GPU-based parallel fireworks algorithm for optimization. In Proceedings of the 15th Annual Conference on Genetic and Evolutionary Computation Conference (ACM, The Netherlands, 2013), pp. 9–16
Y. Pei, S. Zheng, Y. Tan, H. Takagi, An empirical study on influence of approximation approaches on enhancing fireworks algorithm, in Proceedings of the 2012 IEEE Congress on System, Man and Cybernetics (IEEE, 2012), pp. 1322–1327
S. Zheng, A. Janecek, Y. Tan, Enhanced fireworks algorithm, in 2013 IEEE Congress on Evolutionary Computation (CEC) (IEEE, 2013), pp. 2069–2077
J. Li, S. Zheng, Y. Tan, Adaptive Fireworks Algorithm, in Proceedings of IEEE Congress on Evolutionary Computation (CEC’2014) (Beijing, China, 2014), pp. 3214–3221
S. Zheng, A. Janecek, Y. Tan, Dynamic Search in Fireworks Algorithm, in Proceedings of IEEE Congress on Evolutionary Computation (CEC’2014) (Beijing, China, 2014), pp. 3222–3229
Y.J. Zheng, X.L. Xu, H.F. Ling, A hybrid fireworks optimization method with differential evolution. Neurocomputing 148, 75–82 (2012)
C. Yu, L. Kelley, S. Zheng, Y. Tan, Fireworks Algorithm with Differential Mutation for Solving the CEC 2014 Competition Problems, in Proceedings of IEEE Congress on Evolutionary Computation (CEC’2014) (Beijing, China, 2014) pp. 3238–3245
H. Gao, M. Diao, Cultural firework algorithm and its application for digital filters design. Int. J. Model. Identif. Control 14(4), 324–331 (2011)
W. Fang, J. Sun, W. Xu, J. Liu, FIR digital filters design based on quantum-behaved particle swarm optimization, in 2006 IEEE First International Conference on Innovative Computing, Information and Control (ICICIC’06) (IEEE, 2006), vol. 1, pp. 615–619
W. Fang, J. Sun, W.B. Xu, FIR filter design based on adaptive quantum-behaved particle swarm optimization algorithm. Syst. Eng. Electron. 30(7), 1378–1381 (2008)
M. Zhang, B. Zhang, Y. Zheng, A hybrid biogeography-based optimization and fireworks algorithm, in Advances in Swarm Intelligence (Springer, Berlin, 2014), pp. 1–7
J. McCaffrey, Fireworks algorithm optimization, MSDN Mag. 29(12). (2014). http://msdn.microsoft.com/en-us/magazine/dn857364.aspx
Y-J. Zheng, Q. Song, S-Y. Chen, Multiobjective fireworks optimization for variable-rate fertilization in oil crop production. Appl. Soft Comput. 13(11), 4253–4263 (2013)
J. Zhang, On fireworks algorithm for solving 0/1 knapsack problem. J. Wuhan Eng. Inst. 23(3), 64–66 (2011)
A. Janecek, Y. Tan, Swarm intelligence for non-negative matrix factorization. Intern. J. Swarm Int. Res. (IJSIR) 2(4), 12–34 (2011)
W. He, G. Mi, Y. Tan, Parameter optimization of local-concentration model for spam detection by using fireworks algorithm. Advances in Swarm Intelligence (Springer, Berlin 2013), pp. 439–450
S. Zheng, Y. Tan, A unified distance measure scheme for orientation coding in identification, in 2013 IEEE Congress on Information Science and Technology (IEEE, 2013), pp. 979–985
Z. Zheng, Y. Tan, Group explosion strategy for searching multiple targets using swarm robotic, in 2013 IEEE Congress on Evolutionary Computation (IEEE, 2013), pp. 821–828
Y. Tan, Swarm robotics: collective behavior inspired by nature. J. Comput. Sci. Syst. Biol. (JCSB)
Y. Tan, Z.Y. Zheng, Research advance in swarm robotics. Def. Tech. 9(1), 31–62 (2013)
D.U. Zhen-xin, Fireworks algorithm for solving nonlinear equation and system. Mod. Comput. 6(2), 18–21 (2013). doi:10.3969/j.issn.1007-1423.2013.04.005
N. Pholdee, S. Bureerat, Comparative performance of meta-heuristic algorithms for mass minimisation of trusses with dynamic constraints. Adv. Eng. Softw. 75(4), 1–13 (2014). doi:10.1016/j.advengsoft.2014.04.005
I.A. Mohamed, M. Kowsalya, A new power system reconfiguration scheme for power loss minimization and voltage profile enhancement using fireworks algorithm. Electr. Power Energy Syst. 63(4), 461–472 (2014). doi:10.1016/j.ijepes.2014.04.034
I.A. Mohamed, M. Kowsalya, D.P. Kothari, A novel integration technique for optimal network reconfiguration and distributed generation placement in power distribution networks. Electr. Power Energy Syst. 63(6), 461–472 (2014). doi:10.1016/j.ijepes.2014.06.011
R. Rajaram, K. Palanisamy, S. Ramasamy, P. Ramanathan, Selective harmonic elimination in PWM inverter using firefly and fireworks algorithm. Int. J. Innov. Res. Adv. Eng. (IJIRAE) 1(8), 55–62 (2014). doi:10.1016/j.ijepes.2014.06.011. http://www.ijirae.com/volumes/voll/issue8/SPEE10082.08.pdf
A.I. Maswood, S. Wei, M.A. Rahman, A flexible way to generate PWM-SHE switching patterns using genetic algorithm. IEEE SPEC 2, 1130–1134 (2001)
K. Sndareswaran, K. Jayant, T.N. Shanavas, Inverter harmonic elimination through a colony of continuously exploring ants. IEEE Trans. Ind. Electron. 54(10), 2558–2565 (2007)
K. Sndareswaran, V.T. Sreedevi, Inverter harmonic elimination using honey bee intelligence. Aust. J. Electr. Electron. Eng. 6(2) (2009)
N.H. Abdulmajeed, M. Ayob, A firework algorithm for solving capacitated vehicle routing problem. Int. J. Adv. Comput. Tech. (IJACT) 6(1), 79–86 (2014)
Y. Tan, S. Zheng, Research progress on fireworks algorithm. CAAI Trans. Intell. Syst. 9(10), 1–17 (2014)
Y. Tan, C. Yu, S.Q. Zheng, K. Ding, Introduction to fireworks algorithms. Int. J. Swarm Intell. Res. 4(4), 39–70 (2013)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2015 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Tan, Y. (2015). Introduction. In: Fireworks Algorithm. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46353-6_1
Download citation
DOI: https://doi.org/10.1007/978-3-662-46353-6_1
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-46352-9
Online ISBN: 978-3-662-46353-6
eBook Packages: Computer ScienceComputer Science (R0)