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Soft Computing
Erschienen in: Soft Computing 18/2018

05.09.2017 | Focus

Swarm intelligence: past, present and future

verfasst von: Xin-She Yang, Suash Deb, Yu-Xin Zhao, Simon Fong, Xingshi He

Erschienen in: Soft Computing | Ausgabe 18/2018

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Abstract

Many optimization problems in science and engineering are challenging to solve, and the current trend is to use swarm intelligence (SI) and SI-based algorithms to tackle such challenging problems. Some significant developments have been made in recent years, though there are still many open problems in this area. This paper provides a short but timely analysis about SI-based algorithms and their links with self-organization. Different characteristics and properties are analyzed here from both mathematical and qualitative perspectives. Future research directions are outlined, and open questions are also highlighted.
Vorheriger Artikel Special issue: the International Conference on Soft Computing and Machine Intelligence
Nächster Artikel Aggregation of multi-objective fuzzy symmetry-based clustering techniques for improving gene and cancer classification

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Literatur
Alam DF, Yousri DA, Eteiba MB (2015) Flower pollination algorithm based solar PV parameter estimation. Energy Convers Manag 101(2):410–422CrossRef
Ashby WR (1962) Princinples of the self-organizing sysem. In: Von Foerster H, Zopf GW Jr (eds) Principles of self-organization: transactions of the University of Illinois symposium. Pergamon Press, London, pp 255–278
Banks A, Vincent J, Anyakoha C (2008) A review of particle swarm optimization. Part II: hybridisation, combinatorial, multicriteria and constrained optimization, and indicative applications. Nat Comput 7(1):109–124MathSciNetCrossRefMATH
Bekdas G, Nigdeli SM, Yang XS (2015) Sizing optimization of truss structures using flower pollination algorithm. Appl Soft Comput 37:322–331CrossRef
Berlinski D (2001) The advent of the algorithm: the 300-year journey from an idea to the computer. Harvest Book, New York
Bonabeau E, Dorigo M, Theraulaz G (1999) Swarm intelligence: from natural to artificial systems. Oxford University Press, OxfordMATH
Blum C, Roli A (2003) Metaheuristics in combinatorial optimization: overview and conceptural comparision. ACM Comput Surv 35(2):268–308CrossRef
Carbas S (2016) Design optimization of steel frames using an enhanced firefly algorithm. Eng Optim 48(12):2007–2025CrossRef
Chabert JL (1999) A history of algorithms: from the pebble to the microchip. Springer, HeidelbergCrossRef
Clerc M, Kennedy J (2002) The particle swarm: explosion, stability and convergence in a multidimensional compelx space. IEEE Trans Evol Comput 6(1):58–73CrossRef
Corne DW, Reynolds A, Bonabeau E (2012) Swarm intelligence. In: Rozenberg G, Bäck T, Kok JN (eds) Handbook of natural computing. Springer, Heidelberg, pp 1599–1622CrossRef
Cui ZH, Sun B, Wang G, Xue Y, Chen JJ (2017) A novel oriented cuckoo search algorithm to improve DV-Hop performance for cyber-physical systems. J Parallel Distrb Comput 103(1):42–52CrossRef
Darwish SM (2016) Combining firefly algorithm and Bayesian classifier: new direction for automatic multilabel image annotation. IET Image Process 10(10):763–772CrossRef
Eiben AE, Smit SK (2011) Parameter tuning for configuring and analyzing evolutionary aglorithms. Swarm Evol Comput 1(1):19–31CrossRef
Fishman GS (1995) Monte Carlo: concepts, algorithms and applications. Springer, New YorkMATH
Fisher L (2009) The perfect swarm: the science of complexity in everday life. Basic Books, New York
Fister I, Fister I, Yang XS, Brest J (2013a) A comprehensive review of firefly algorithms. Swarm Evol Comput 13(1):34–46CrossRef
Fister I, Yang XS, Brest J, Fister I Jr (2013b) Modified firefly algorithm using quaternion representation. Expert Syst Appl 40(18):7220–7230CrossRef
Gálvez A, Iglesias A (2016) New memetic self-adaptive firefly algorithm for continuous optimisation. Int J Bio Inspired Comput 8(5):300–317CrossRef
Gandom AH, Yang XS, Alavi AH (2013) Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng Comput 29(1):17–35CrossRef
Ghate A, Smith R (2008) Adaptive search with stochastic acceptance probabilities for global optimization. Oper Res Lett 36(3):285–290MathSciNetCrossRefMATH
Goldberg DE (1989) Genetic algorithms in search, optimisation and machine learning, reading. Addison Wesley, Boston
He XS, Yang XS, Karamanoglu M, Zhao YX (2017) Global convergence analysis of the flower pollination algorithm: a discrete-time Markov chain approach. Procedia Comput Sci 108:1354–1363CrossRef
Fong S, Deb S, Yang XS (2015) A heuristic optimization method inspired by wolf preying behavior. Neural Comput Appl 26(7):1725–1738CrossRef
Kashi S, Minuchehr A, Poursalehi N, Zolfaghari A (2014) Bat algorithm for the fuel arrangement optimization of reactor core. Ann Nucl Energy 64:144–151CrossRef
Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks, IEEE Press, Piscataway, NJ, pp 1942–1948
Keller EF (2009) Organisms, machines, and thunderstorms: a history of self-organization, part two. Complexity, emergenece, and stable attractors. Hist Stud Nat Sci 39(1):1–31CrossRef
Khare A, Rangnekar S (2013) A review of particle swarm optimization and its applications in solar photovoltaic system. Appl Soft Comput 13(5):2997–3006CrossRef
Ma JM, Ting TO, Man KL, Zhang N, Guan SU, Wong PWH (2013) Parameter estimation of photovoltaic models via cuckoo search. Appl Math 2013, Article ID 362619. doi:10.​1155/​2013/​362619
Marichelvam M, Prabaharan T, Yang XS (2014a) Improved cuckoo search algorithm for hybrid flow shop scheduling problems to minimize makespan. Appl Soft Comput 19(1):93–101CrossRef
Marichelvam MK, Thirumoorthy P, Yang XS (2014b) A discrete firefly algorithm for the multi-objective hybrid flowshop scheduling problems. IEEE Trans Evol Comput 18(2):301–305CrossRef
Miller P (2007) Swarm theory. National Geographic
Osaba E, Yang XS, Diaz F, Onieva E, Masegosa AD, Perallos A (2016a) A discrete firefly algorithm to sole a rich vehicle routing problem modelling a newspaper distribution system with recycling policy. Soft Comput. doi:10.​1007/​s00500-016-2114-1
Osaba E, Yang XS, Diaz F, Lopez-Garcia P, Carballedo R (2016b) An improved discrete bat algorithm for symmetric and assymmetric traveling salesman problems. Eng Appl Artif Intell 48(1):59–71CrossRef
Ouaarab A, Ahiod B, Yang XS (2015) Random-key cuckoo search for the travelling salesman problem. Soft Comput 19(4):1099–1106CrossRef
Parsopoulos KE, Vrahatis MN (2010) Particle swarm optimization and intelligence: advances and applications. Information Science Publishing (IGI Global), London
Reyes-Sierra M, Coello Coello CA (2006) Multi-objective particle swarm optimizers: a survey of the state-of-the-art. Int J Comput Intell Res 2(3):287–308MathSciNet
Rodrigues D, Silva GF, Papa JP, Marana AN, Yang XS (2016) EEG-based person identification through binary flower pollination algorithm. Expert Syst Appl 62(1):81–90CrossRef
Rodrigues D, Pereira LAM, Nakamura RYM, Costa KAP, Yang XS, Souza AN, Papa JP (2014) A wrapper approach for feature selection based on bat algorithm and optimum-path forest. Expert Syst Appl 41(5):2250–2258CrossRef
Rodrigues D, Silva GF, Papa JP, Marana AN, Yang XS (2016) EEG-based person identification through binary flower pollination algorithm. Expert Syst Appl 62(1):81–90CrossRef
Rodrigues D, Silva GF, Papa JP, Marana AN, Yang XS (2016) EEG-based person identification through binary flower pollination algorithm. Expert Syst Appl 62(1):81–90CrossRef
Senthinath J, Omkar SN, Mani V (2011) Clustering using firefly algorithm: performance study. Swarm Evol Comput 1(3):164–171CrossRef
Soleimani A (2015) Combined particle swarm optimization and canonical sign digit to design finite impulse response filter. Soft Comput 19(2):407–419CrossRef
Storn R, Price K (1997) Differential evolution: a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–59MathSciNetCrossRefMATH
Surowiecki J (2004) The wisdom of crowds. Anchor Books, New York
Süli E, Mayer D (2003) An introduction to numerical analysis. Cambridge University Press, CambridgeCrossRef
Suzuki JA (1995) A Markov chain analysis on simple genetic algorithms. IEEE Trans Syst Man Cybern 25(4):655–9CrossRef
Tilahun SL, Ngnotechouye JMT (2017) Firefly algorithm for discrete optimization problems: a survey. KSCE J Civ Eng 21(2):535–545CrossRef
Ting O, Yang XS, Cheng S, Huang KZ (2015) Hybrid metaheuristic algorithms: past, present, and futute. In: Yang XS (ed) Recent advances in swarm intelligence and evolutionary computation. Studies in computational intelligence, vol 585, pp 71–83
Villalobos-Arias M, Colleo CAC, Hernández-Lerma O (2005) Asypmotic convergence of metaheuristics for multiobjective optimization problems. Soft Comput 10(11):1001–5CrossRef
Wang H, Wang WJ, Zhou XY, Sun H, Zhao J, Yu X, Cui ZH (2017) Firefly algorithm with neighborhood attraction. Inf Sci 382–383(1):374–387CrossRef
Wolpert DH, Macready WG (1997) No free lunch theorem for optimization. IEEE Trans Evol Comput 1(1):67–82CrossRef
Wolpert DH, Macready WG (2005) Coevolutionary free lunches. IEEE Trans Evol Comput 9(6):721–735CrossRef
Yang XS (2011) Bat algorithm for multi-objective optimisation. Int J Bio Inspired Comput 3(5):267–274CrossRef
Yang XS, He S (2013a) Bat algorithm: literature review and applications. Int J Bio Inspired Comput 5(3):141–149CrossRef
Yang XS, Deb S, Loomes M, Karamanoglu M (2013) A framework for self-tuning optimization algorithm. Neural Comput Appl 23(7–8):2051–2057CrossRef
Yang XS, Karamanoglu M, He XS (2014) Flower pollination algorithm: a novel approach for multiobjective optimization. Eng Optim 46(9):1222–1237MathSciNetCrossRef
Yang XS, Deb S, Fong S, He XS, Zhao YX (2016) From swarm intelligence to metaheuristics: nature-inspired optimization algorithms. Computer 49(9):52–59CrossRef
Zaharie D (2009) Influence of crossover on the behaviour of the differential evolution algorithm. Appl Soft Comput 9(3):1126–38CrossRef
Zhao CX, Wu CZ, Chai J, Wang XY, Yang XM, Lee M, Kim MJ (2017) Decomposition-based multi-objective firefly algorithm for RFID network planning with uncertainy. Appl Soft Comput 55:549–564CrossRef
Zhou Y, Wang R, Luo Q (2016) Elite opposition-based flower pollinaton algorithm. Neurocomputing 188:294–310CrossRef
Zineddine M (2015) Vulnerabilities and mitigation techniques toning in the cloud: a cost and vulnerabilities coverage optimiation approach using cuckoo search algorithm with Lévy flights. Comput Secur 48(1):1–18CrossRef
Zouache D, Nouioua F, Moussaoui A (2016) Quantum-inspired firefly algorithm with particle swarm optimization for discrete optimization problems. Soft Comput 20(7):2781–2799CrossRef
Metadaten
Titel
Swarm intelligence: past, present and future
verfasst von
Xin-She Yang
Suash Deb
Yu-Xin Zhao
Simon Fong
Xingshi He
Publikationsdatum
05.09.2017
Verlag
Springer Berlin Heidelberg
Erschienen in
Soft Computing / Ausgabe 18/2018
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-017-2810-5

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