Abstract
Chemical Reaction Optimization (CRO) is a recently established metaheuristics for optimization, inspired by the nature of chemical reactions. A chemical reaction is a natural process of transforming the unstable substances to the stable ones. In microscopic view, a chemical reaction starts with some unstable molecules with excessive energy. The molecules interact with each other through a sequence of elementary reactions. At the end, they are converted to those with minimum energy to support their existence. This property is embedded in CRO to solve optimization problems. CRO can be applied to tackle problems in both the discrete and continuous domains. We have successfully exploited CRO to solve a broad range of engineering problems, including the quadratic assignment problem, neural network training, multimodal continuous problems, etc. The simulation results demonstrate that CRO has superior performance when compared with other existing optimization algorithms. This tutorial aims to assist the readers in implementing CRO to solve their problems. It also serves as a technical overview of the current development of CRO and provides potential future research directions.
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References
AlRashidi M, El-Hawary M (2009) A survery of particle swarm optimization applications in electric power systems. IEEE Trans Evol Comput 13(4): 913–918
Ashlock D (2004) Evolutionary computation for modeling and optimization. Springer, New York
Boyd S, Vandenberghe L (2004) Convex optimization. Cambridge University Press, Cambridge, UK
Burger R (2000) The mathematical theory of selection, recombination, and mutation. Wiley, Chichester
Cela E (1998) The quadratic assignment problem: theory and algorithms. Kluwer Academic Publishers, Dordrecht, The Netherlands
Chen XS, Ong YS, Lim MH, Tan KC (2011) A multi-facet survey on memetic computation. IEEE Trans Evol Comput 15(5): 591–607
Demeulemeester EL, Herroelen WS (2002) Project scheduling: a research handbook. Academic Publishers, Boston, MA, USA
Dorigo M, Stutzle T (2004) Ant colony optimization. The MIT Press, Cambridge, MA, USA
Eiben AE (2001) Evolutionary algorithms and constraint satisfaction: definitions, survey, methodology, and research directions. Theoretical aspects of evolutionary computing. Springer, London, pp, pp 13–30
Fortnow L (2009) The status of the P versus NP problem. Commun ACM 52(9): 78–86
Garey MR, Johnson DS (1979) Computers and intractability: A guide to the theory of NP-completeness. WH Freeman & Co Ltd, New York
Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76(2): 60–68
Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, Reading, MA, USA
Guggenheim EA (1967) Thermodynamics: an advanced treatment for chemists and physicists. 5th edn. Wiley, North Holland
Kennedy J, Eberhart RC (2001) Swarm intelligence. Morgan Kaufmann, San Francisco
Kim M, Medard M, Aggarwal V, OReilly UM, Kim W, Ahn CW (2007) Evolutionary approaches to minimizing network coding resources. In: Proceedings of the 26th annual IEEE conference on computer Communications, Anchorage, AK, USA
Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220((4598): 671–680
Lam AYS, Li VOK (2010) Chemical-reaction-inspired metaheuristic for optimization. IEEE Trans Evol Comput 14(3): 381–399
Lam AYS, Xu J, Li VOK (2010) Chemical reaction optimization for population transition in peer-to-peer live streaming. In: Proceedings of the IEEE congress on evolutionary computation. Barcelona, Spain
Lam AYS, Li VOK, Yu JJQ (2011, in press) Real-coded chemical reaction optimization. IEEE Trans Evol Comput (accepted for publication)
Lam AYS, Li VOK (2010) Chemical reaction optimization for cognitive radio spectrum allocation. In: Proceedings of the IEEE Global Communications Conference. Miami, FL, USA
Loiola EM, de Abreu NMM, Boaventura-Netto PO, Hahn P, Querido T (2007) A survey for the quadratic assignment problem. Eur J Oper Res 176(2): 657–690
Ong YS, Lim MH, Chen XS (2010) Research frontier: memetic computation past, present and future. IEEE Comput Intell Mag 5(2): 24–36
Palmes PP, Hayasaka T, Usui S (2005) Mutation-based genetic neural network. IEEE Trans Neural Netw 16(3): 587–600
Pan B, Lam AYS, Li VOK (2011) Network coding optimization based on chemical reaction optimization. In: Proceedings of the IEEE global communications conference. Houston, TX, USA
Peng C, Zheng H, Zhao BY (2006) Utilization and fairness in spectrum assignment for opportunistic spectrum access. ACM/Kluwer Mobile Netw Appl 11(4): 555–576
Ritchie G, Levine J (2004) A hybrid ant algorithm for scheduling independent jobs in heterogeneous computing environments. In: Proceedings of 23rd workshop of the UK planning and scheduling special interest group. Cork, Ireland
Price K, Storn R, Lampinen J (2005) Differential evolution: a practical approach to global optimization. Springer, Berlin
Rogers H (1987) Theory of recursive functions and effective computability. The MIT Press, Cambridge, MA, USA
Schach S (2010) Object-oriented and classical software engineering. 8th edn. McGraw-Hill, New York
Shadbolt N (2004) Nature-inspired computing. IEEE Intell Syst 19(1): 2–3
Shin SY, Lee IH, Kim D, Zhang BT (2005) Multiobjective evolutionary optimization of DNA sequences for reliable DNA computing. IEEE Trans Evol Comput 9(2): 143–158
Subramanian AP, Gupta H, Das SR, Cao J (2008) Minimum interference channel assignment in multiradio wireless mesh networks. IEEE Trans Mobile Comput 7(12): 1459–1473
Tollo GD, Roli A (2008) Metaheuristics for the portfolio selection problem. J Financial Quant Anal 8(4): 621–636
Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1): 67–82
Xu J, Lam AYS, Li VOK (2010) Chemical reaction optimization for the grid scheduling problem. In: Proceedings of the IEEE international conference on communications. Cape Town, South Africa
Xu J, Lam AYS, Li VOK (2011) Chemical reaction optimization for task scheduling in grid computing. IEEE Trans Parallel Distrib Syst 22(10): 1624–1631
Xu J, Lam AYS, Li VOK (2011) Stock portfolio selection using chemical reaction optimization. In: Proceedings of the international conference on operations research and financial engineering. Paris, France
Xu J, Lam AYS, Li VOK (2010) Parallel chemical reaction optimization for the quadratic assignment problem. In: Proceedings of the international conference on genetic and evolutionary methods. Las Vegas, NV, USA
Yao X, Liu Y, Lin G (1999) Evolutionary programming made faster. IEEE Trans Evol Comput 3(2): 82–102
Yu JJQ, Lam AYS, Li VOK (2011) Evolutionary artificial neural network based on chemical reaction optimization. In: Proceedings of the IEEE congress on evolutionary computation. New Orleans, LA, USA
Yu L, Chen H, Wang S, Lai KK (2009) Evolving least squares support vector machines for stock market trend mining. IEEE Trans Evol Comput 13(1): 87–102
Acknowledgments
This work was supported in part by the Strategic Research Theme of Information Technology of The University of Hong Kong. A.Y.S. Lam was also supported in part by the Croucher Foundation Research Fellowship.
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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Lam, A.Y.S., Li, V.O.K. Chemical Reaction Optimization: a tutorial. Memetic Comp. 4, 3–17 (2012). https://doi.org/10.1007/s12293-012-0075-1
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DOI: https://doi.org/10.1007/s12293-012-0075-1