Abstract
2-Chlorophenol is a kind of representative organic waste water. With the environmental pollution becoming increasingly serious, and the large amount of waste discharged and the increasing difficulty of treatment, the research on the kinetics of the oxidation of supercritical water of 2-chlorophenol has important significant. Aiming at the phenomenon that the Glowworm Swarm Optimization (GSO) algorithm has slow convergence, low precision and easy to get trapped into local optimum, this paper presents an improved version of the GSO based on the behavior of predator-prey and biological predator, and we call it dual population Glowworm Swarm Optimization (GSOPP). The algorithm accelerates the convergence speed by introducing strategies such as chase and escape and variation among populations, and can obtain a more accurate solution. Tested by three standard test functions, the results showed that the improved GSOPP algorithm had better performance than the basic GSO algorithm. Finally, the algorithm was applied to estimate the parameter estimation of the supercritical water oxidation kinetics of 2-chlorophenol, and satisfactory results were obtained.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Li, R., Savage, P.E., Szmukler, D.: 2-Chlorophenol oxidation in supercritical water: global kinetics and reaction products. AIChE J. 39, 178–187 (1993)
Krishnanand, K.N., Ghose, D.: Glowworm swarm optimization: a new method for optimizing multi-modal functions. Int. J. Comput. Intell. Stud. 1(1), 93–119 (2009)
Krishnanand, K.N.: Glowworm swarm optimization: a multimodal function optimization paradigm with applications to multiple signal source localization tasks. Indian Institute of Science [S. l.] (2007)
Krishnanand, K.N., Ghose, D.A.: Glowworm swarm optimization based multi-robot system for signal source localization. Berlin, Germany [s.n.] (2009)
Krishnanand, K.N., Ghose, D.: Chasing multiple mobile signal sources: a glowworm swarm optimization approach. In: Proceedings of the 3rd Indian International Conference on Artificial Intelligence [S. l.]. IEEE Press (2007)
Yang, Y., Zhou, Y.: Glowworm swarm optimization algorithm for solving numerical integral. Commun. Comput. Inf. Sci. 13(4), 389–394 (2011)
Gong, Q., Zhou, Y., Yang, Y.: Artificial glowworm swarm optimization algorithm for solving 0-1 knapsack problem. Adv. Mater. Res. 144(143), 166–171 (2011)
Shang, Y.: Predatory behaviour in animals. Bull. Biol. 36(2), 13–14 (2001)
Ward, Z.: The importance of certain assemblages of iers as “information-center” for food finding. Ibis 115, 517–531 (1973)
Roberts, D.: Imitation and suggestion in animals. Bull. Animal Behav. 1, 11–19 (1941)
Yuan, M.: Animal predation strategy and anti-predator strategy. Educ. Sci. 6(15), 87 (2009)
He, Y., Chen, D., Wu, X.: Estimation of kinetic parameters using hybrid ant colony system. J. Chem. Ind. Eng. (China) 56(3), 487–491 (2005)
Yan, X., Chen, D., Hu, S., Ding, J.: Estimation of kinetic parameters using chaos genetic algorithms. J. Chem. Ind. Eng. (China) 53(8), 810–814 (2002)
Acknowledgments
This work is supported by the Project supported by the National Natural Science Foundation of China (Grant No. 21466008, 21566007).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Mo, Y., Lu, Y., Liu, F. (2018). Predator-Prey Behavior Firefly Algorithm for Solving 2-Chlorophenol Reaction Kinetics Equation. In: Tan, Y., Shi, Y., Tang, Q. (eds) Advances in Swarm Intelligence. ICSI 2018. Lecture Notes in Computer Science(), vol 10941. Springer, Cham. https://doi.org/10.1007/978-3-319-93815-8_43
Download citation
DOI: https://doi.org/10.1007/978-3-319-93815-8_43
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-93814-1
Online ISBN: 978-3-319-93815-8
eBook Packages: Computer ScienceComputer Science (R0)