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2014 | OriginalPaper | Buchkapitel

Cuckoo Search and Firefly Algorithm: Overview and Analysis

verfasst von : Xin-She Yang

Erschienen in: Cuckoo Search and Firefly Algorithm

Verlag: Springer International Publishing

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Abstract

Firefly algorithm (FA) was developed by Xin-She Yang in 2008, while cuckoo search (CS) was developed by Xin-She Yang and Suash Deb in 2009. Both algorithms have been found to be very efficient in solving global optimization problems. This chapter provides an overview of both cuckoo search and firefly algorithm as well as their latest developments and applications. We analyze these algorithms and gain insight into their search mechanisms and find out why they are efficient. We also discuss the essence of algorithms and its link to self-organizing systems. In addition, we also discuss important issues such as parameter tuning and parameter control, and provide some topics for further research.

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Nächstes Kapitel On the Randomized Firefly Algorithm
Fußnoten
Literatur
1.
Abshouri, A.A., Meybodi, M.R., Bakhtiary, A.: New firefly algorithm based on multiswarm and learning automata in dynamic environments. Third international conference on signal processing systems (ICSPS2011), pp. 73–77. Yantai, China, 27–28 Aug 2011
2.
Azad, S.K., Azad, S.K.: Optimum design of structures using an improved firefly algorithm. Int. J. Optim. Civ. Eng. 1(2), 327–340 (2011)
3.
4.
Ashby, W.R.: Princinples of the self-organizing sysem. In: Von Foerster, H., Zopf Jr, G.W. (eds.) Pricinples of Self-Organization: Transactions of the University of Illinois Symposium, pp. 255–278. Pergamon Press, London, UK (1962)
5.
Banati, H., Bajaj, M.: Firefly based feature selection approach. Int. J. Comput. Sci. Issues 8(2), 473–480 (2011)
6.
Bansal, J.C., Deep, K.: Optimisation of directional overcurrent relay times by particle swarm optimisation. In: Swarm intelligence symposium (SIS 2008), pp. 1–7. IEEE Publication (2008)
7.
Basu, B., Mahanti, G.K.: Firefly and artificial bees colony algorithm for synthesis of scanned and broadside linear array antenna. Prog. Electromagn. Res. B 32, 169–190 (2011)CrossRef
8.
Bénichou, O., Loverdo, C., Moreau, M., Voituriez, R.: Two-dimensional intermittent search processes: An alternative to Lévy flight strategies. Phys. Rev. E74, 020102(R) (2006)
9.
Bhargava, V., Fateen, S.E.K., Bonilla-Petriciolet, A.: Cuckoo search: a new nature-inspired optimization method for phase equilibrium calculations. Fluid Phase Equilib. 337, 191–200 (2013)CrossRef
10.
Blum, C., Roli, A.: Metaheuristics in combinatorial optimisation: overview and conceptural comparision. ACM Comput. Surv. 35, 268–308 (2003)CrossRef
11.
Bulatović, R.R., Bordević, S.R., Dordević, V.S.: Cuckoo search algorithm: a metaheuristic approach to solving the problem of optimum synthesis of a six-bar double dwell linkage. Mech. Mach. Theory 61, 1–13 (2013)CrossRef
12.
Chandrasekaran, K., Simon, S.P.: Multi-objective scheduling problem: hybrid appraoch using fuzzy assisted cuckoo search algorithm. Swarm Evol. Comput. 5(1), 1–16 (2012)CrossRef
13.
Chatterjee, A., Mahanti, G.K., Chatterjee, A.: Design of a fully digital controlled reconfigurable switched beam conconcentric ring array antenna using firefly and particle swarm optimisation algorithm. Prog. Elelectromagn. Res. B 36, 113–131 (2012)CrossRef
14.
Chifu, V.R., Pop, C.B., Salomie, I., Suia, D.S., Niculici, A.N.: Optimizing the semantic web service composition process using cuckoo search. In: Intelligent distributed computing V, studies in computational intelligence vol. 382, pp. 93–102 (2012)
15.
Choudhary, K., Purohit, G.N.: A new testing approach using cuckoo search to achieve multi-objective genetic algorithm. J. Comput. textbf3(4), 117–119 (2011)
16.
Clerc, M., Kennedy, J.: The particle swarm—explosion, stability, and convergence in a multidimensional complex space. IEEE Trans. Evol. Comput. 6(1), 58–73 (2002)CrossRef
17.
Civicioglu, P., Besdok, E.: A conception comparison of the cuckoo search, particle swarm optimization, differential evolution and artificial bee colony algorithms. Artif. Intell. Rev. (2011). doi:10.​1007/​s10462-011-92760
18.
dos Santos Coelho, L., de Andrade Bernert, D.L., Mariani, V.C.: A chaotic firefly algorithm applied to reliability-redundancy optimisation. In: 2011 IEEE Congress on evolutionary computation (CEC’11), pp. 517–521 (2011)
19.
Dhivya, M., Sundarambal, M., Anand, L.N.: Energy efficient computation of data fusion in wireless sensor networks using cuckoo based particle approach (CBPA). Int. J. Commun. Netw. Syst. Sci. 4, 249–255 (2011)
20.
Dhivya, M., Sundarambal, M.: Cuckoo search for data gathering in wireless sensor networks. Int. J. Mobile Commun. 9, 642–656 (2011)CrossRef
21.
Durgun, I., Yildiz, A.R.: Structural design optimization of vehicle components using cuckoo search algorithm. Mater. Test. 3, 185–188 (2012)CrossRef
22.
Durkota, K.: Implementation of a discrete firefly algorithm for the QAP problem within the sage framework. B.Sc. thesis, Czech Technical University (2011)
23.
Eiben, A.E., Smit, S.K.: Parameter tuning for configuring and analyzing evolutionary algorithms. Swarm Evol. Comput. 1, 19–31 (2011)CrossRef
24.
Farahani, S.M., Abshouri, A.A., Nasiri, B., Meybodi, M.R.: A Gaussian firefly algorithm. Int. J. Mach. Learn. Comput. 1(5), 448–453 (2011)CrossRef
25.
Farahani, S.M., Nasiri, B., Meybodi, M.R.: A multiswarm based firefly algorithm in dynamic environments. In: Third international conference on signal processing systems (ICSPS2011), pp. 68–72. Yantai, China, 27–28 Aug 2011
26.
Fister Jr, I., Fister, I., Brest, J., Yang, X.S.: Memetic firefly algorithm for combinatorial optimisation. In: Filipič, B., Šilc, J. (eds.) Bioinspired Optimisation Methods and Their Applications (BIOMA2012), pp. 75–86. Bohinj, Slovenia, 24–25 May 2012
27.
28.
29.
Gandomi, A.H., Yang, X.S., Talatahari, S., Deb, S.: Coupled eagle strategy and differential evolution for unconstrained and constrained global optimization. Comput. Math. Appl. 63(1), 191–200 (2012)MathSciNetCrossRefMATH
30.
Giannakouris, G., Vassiliadis, V., Dounias, G.: Experimental study on a hybrid nature-inspired algorithm for financial portfolio optimisation, SETN 2010. Lecture Notes in Artificial Intelligence (LNAI 6040), pp. 101–111 (2010)
31.
Hassanzadeh, T., Vojodi, H., Moghadam, A.M.E.: An image segmentation approach based on maximum variance intra-cluster method and firefly algorithm. In: Proceedings of 7th International Conference on Natural Computation (ICNC2011), pp. 1817–1821 (2011)
32.
Horng, M.-H., Lee, Y.-X., Lee, M.-C., Liou, R.-J.: Firefly metaheuristic algorithm for training the radial basis function network for data classification and disease diagnosis. In: Parpinelli, R., Lopes, H.S. (eds.) Theory and New Applications of Swarm Intelligence, pp. 115–132 (2012)
33.
Horng, M.-H.: Vector quantization using the firefly algorithm for image compression. Expert Syst. Appl. 39, 1078–1091 (2012)CrossRef
34.
Horng, M.-H., Liou, R.-J.: Multilevel minimum cross entropy threshold selection based on the firefly algorithm. Expert Syst. Appl. 38, 14805–14811 (2011)CrossRef
35.
Jati, G.K., Suyanto, S.: Evolutionary discrete firefly algorithm for travelling salesman problem, ICAIS2011. Lecture Notes in Artificial Intelligence (LNAI 6943), pp. 393–403 (2011)
36.
Jiang, M., Luo, Y.P., Yang, S.Y.: Stochastic convergence analysis and parameter selection of the standard particle swarm optimization algorithm. Inf. Process. Lett. 102, 8–16 (2007)MathSciNetCrossRefMATH
37.
38.
Keller, E.F.: Organisms, machines, and thunderstorms: a history of self-organization, part two. Complexity, emergenece, and stable attractors. Hist. Stud. Nat. Sci. 39(1), 1–31 (2009)
39.
Kennedy, J., Eberhart, R.C.: Particle swarm optimization., In: Proceedings of IEEE International Conference on Neural Networks, pp. 1942–1948. Piscataway, NJ (1995)
40.
Koziel, S., Yang, X.S.: Computational Optimization, Methods and Algorithms. Springer, Germany (2011)CrossRefMATH
41.
Kumar A., Chakarverty, S.: Design optimization for reliable embedded system using Cuckoo Search. In: Proceedings of 3rd International Conference on Electronics Computer Technology (ICECT2011), pp. 564–568 (2011)
42.
Layeb, A.: A novel quantum-inspired cuckoo search for Knapsack problems. Int. J. Bio-inspired Comput. 3(5), 297–305 (2011)
43.
Moravej, Z., Akhlaghi, A.: A novel approach based on cuckoo search for DG allocation in distribution network. Electr Power Energy Syst. 44, 672–679 (2013)CrossRef
44.
Nandy, S., Sarkar, P.P., Das, A.: Analysis of nature-inspired firefly algorithm based back-propagation neural network training. Int. J. Comput. Appl. 43(22), 8–16 (2012)
45.
Noghrehabadi, A., Ghalambaz, M., Vosough, A.: A hybrid power series—Cuckoo search optimization algorithm to electrostatic deflection of micro fixed-fixed actuators. Int. J. Multi. Sci. Eng. 2(4), 22–26 (2011)
46.
Palit, S., Sinha, S., Molla, M., Khanra, A., Kule, M.: A cryptanalytic attack on the knapsack cryptosystem using binary Firefly algorithm. In: 2nd International Conference on Computer and Communication Technology (ICCCT), pp. 428–432. India, 15–17 Sept 2011
47.
Parpinelli, R.S., Lopes, H.S.: New inspirations in swarm intelligence: a survey. Int. J. Bio-Inspired Comput. 3, 1–16 (2011)CrossRef
48.
49.
Perumal, K., Ungati, J.M., Kumar, G., Jain, N., Gaurav, R., Srivastava, P.R.: Test data generation: a hybrid approach using cuckoo and tabu search, Swarm, Evolutionary, and Memetic Computing (SEMCCO2011). Lecture Notes in Computer Sciences vol. 7077, pp. 46–54 (2011)
50.
Rajini, A., David, V.K.: A hybrid metaheuristic algorithm for classification using micro array data. Int. J. Sci. Eng. Res. 3(2), 1–9 (2012)
51.
Rampriya, B., Mahadevan, K., Kannan, S.: Unit commitment in deregulated power system using Lagrangian firefly algorithm. In: Proceedings of IEEE International Conference on Communication Control and Computing Technologies (ICCCCT2010), pp. 389–393 (2010)
52.
Ren, Z.H., Wang, J., Gao, Y.L.: The global convergence analysis of particle swarm optimization algorithm based on Markov chain. Control Theory Appl. (in Chinese) 28(4), 462–466 (2011)MATH
53.
Sayadi, M.K., Ramezanian, R., Ghaffari-Nasab, N.: A discrete firefly meta-heuristic with local search for makespan minimization in permutation flow shop scheduling problems. Int. J. Ind. Eng. Comput. 1, 1–10 (2010)CrossRef
54.
Senthilnath, J., Omkar, S.N., Mani, V.: Clustering using firely algorithm: performance study. Swarm Evol. Comput. 1(3), 164–171 (2011)CrossRef
55.
Speed, E.R.: Evolving a Mario agent using cuckoo search and softmax heuristics. In: Proceedings of the Games Innovations Conference (ICE-GIC), pp. 1–7 (2010)
56.
Srivastava, P.R., Chis, M., Deb, S., Yang, X.S.: An efficient optimization algorithm for structural software testing. Int. J. Artif. Intell. 9(S12), 68–77 (2012)
57.
Taweewat, P., Wutiwiwatchai, C.: Musical pitch estimation using a supervised single hidden layer feed-forward neural network. Expert Syst. Appl. 40, 575–589 (2013)CrossRef
58.
Tein, L.H., Ramli, R.: Recent advancements of nurse scheduling models and a potential path. In: Proceedings of 6th IMT-GT Conference on Mathematics, Statistics and its Applications (ICMSA 2010), pp. 395–409 (2010)
59.
Valian, E., Mohanna, S., Tavakoli, S.: Improved cuckoo search algorithm for feedforward neural network training. Int. J. Artif. Intell. Appl. 2(3), 36–43 (2011)
60.
Valian, E., Tavakoli, S., Mohanna, S., Haghi, A.: Improved cuckoo search for reliability optimization problems. Comput. Ind. Eng. 64, 459–468 (2013)CrossRef
61.
Vazquez, R.A.: Training spiking neural models using cuckoo search algorithm. In: 2011 IEEE Congress on Eovlutionary Computation (CEC’11), pp. 679–686 (2011)
62.
Walton, S., Hassan, O., Morgan, K., Brown, M.R.: Modified cuckoo search: a new gradient free optimization algorithm. Chaos, Solitons Fractals 44(9), 710–718 (2011)CrossRef
63.
Wang, F., He, X.-S., Wang, Y., Yang, S.M.: Markov model and convergence analysis based on cuckoo search algorithm. Comput. Eng. 38(11), 180–185 (2012)
64.
Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1, 67–82 (1997)CrossRef
65.
Yang, X.S.: Nature-Inspired Metaheuristic Algorithms. Luniver Press, UK (2008)
66.
Yang, X.S.: Introduction to Computational Mathematics. World Scientific Publishing, Singapore (2008)CrossRefMATH
67.
Yang, X.S.: Engineering Optimisation: An Introduction with Metaheuristic Applications. John Wiley and Sons, USA (2010)CrossRef
68.
Yang, X.S.: A new metaheuristic bat-inspired algorithm. In: Gonzalez, J.R. et al. (eds.) Nature Inspired Cooperative Strategies for Optimisation (NICSO 2010). Studies in Computational Intelligence , vol.28, 4 pp. 65–74. Springer, Berlin (2010)
69.
Yang, X.S., Deb, S.: Eagle strategy using Lévy walks and firefly algorithm for stochastic optimization. In: Gonzalez, J.R. et al. (eds.) Nature-Inspired Cooperative Strategies for Optimization (NICSO 2010), Studies in Computational Intelligence, vol. 284, pp. 101–111. Springer, Berlin (2010)
70.
Yang, X.S.: Firefly algorithms for multimodal optimization. In: Stochastic Algorithms: Foundations and Applications, SAGA 2009. Lecture Notes in Computer Sciences, vol. 5792, pp. 169–178 (2009)
71.
Yang, X.-S.: Firefly algorithm, stochastic test functions and design optimisation. Int. J. Bio-inspired Comput. 2(2), 78–84 (2010)CrossRef
72.
Yang, X.S., Deb, S., Fong, S.: Accelerated particle swarm optimization and support vector machine for business optimization and applications, Networked Digital Technologies 2011. Commun. Comput. Inf. Sci. 136, 53–66 (2011)
73.
Yang, X.S., Gandomi, A.H.: Bat algorithm: a novel approach for global engineering optimization. Eng. Comput. 29(5), 1–18 (2012)MATH
74.
Yang, X.S.: Flower pollination algorithm for global optimization. In: Unconventional Computation and Natural Computation, pp. 240–249. Springer (2012)
75.
Yang, X.S., Karamanoglu, M., He, X.S.: Multi-objective flower algorithm for optimization. Procedia Comput. Sci. 18, 861–868 (2013)CrossRef
76.
Yang, X.S., Deb, S.: Cuckoo search via Lévy flights. In: Proceeings of World Congress on Nature & Biologically Inspired Computing (NaBIC 2009), pp. 210–214. IEEE Publications, USA (2009)
77.
Yang, X.S.: Chaos-enhanced firefly algorithm with automatic parameter tuning. Int. J. Swarm Intell. Res. 2(4), 1–11 (2011)CrossRef
78.
Yang, X.S., Deb, S., Fong, S.: Accelerated particle swarm optimization and support vector machine for business optimization and applications, Networked Digital Technologies (NDT’2011). Commun. Comput. Inform. Sci. 136(Part I), 53–66 (2011)
79.
Yang, X.S.: Multiobjective firefly algorithm for continuous optimization. Engineering with Computers 29(2), 175–184 (2013)CrossRef
80.
Yang, X.S., Deb, S.: Engineering optimization by cuckoo search. Int. J. Math. Model. Num. Opt. 1(4), 330–343 (2010)
81.
Yang, X.S., Deb, S.: Multiobjective cuckoo search for design optimization. Comput. Oper. Res. 40(6), 1616–1624 (2013)MathSciNetCrossRef
82.
Yang, X.S., Cui, Z.H., Xiao, R.B., Gandomi, A.H., Karamanoglu, M.: Swarm Intelligence and Bio-inspired Computation: Theory and Applications. Elsevier, Waltham (2013)
83.
Yildiz, A.R.: Cuckoo search algorithm for the selection of optimal machine parameters in milling operations. Int. J. Adv. Manuf. Technol. (2012). doi:10.​1007/​s00170-012-4013-7
84.
Yousif, A., Abdullah, A.H., Nor, S.M., Abdelaziz, A.A.: Scheduling jobs on grid computing using firefly algorithm. J. Theor. Appl. Inform. Technol. 33(2), 155–164 (2011)
85.
Zaman, M.A., Matin, M.A.: Nonuniformly spaced linear antenna array design using firefly algorithm. Int. J. Microw. Sci. Technol. 2012, 8 (2012). Article ID: 256759, doi:10.​1155/​2012/​256759
86.
Zheng, H.Q., Zhou, Y.: A novel cuckoo search optimization algorithm based on Gauss distribution. J. Comput. Inform. Syst. 8, 4193–4200 (2012)
Metadaten
Titel
Cuckoo Search and Firefly Algorithm: Overview and Analysis
verfasst von
Xin-She Yang
Copyright-Jahr
2014
Buch
Cuckoo Search and Firefly Algorithm

Print ISBN: 978-3-319-02140-9

Electronic ISBN: 978-3-319-02141-6

Copyright-Jahr: 2014

DOI
https://doi.org/10.1007/978-3-319-02141-6_1