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Event-Triggering Impulsive Differential Evolution

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Handbook of Real-Time Computing

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Abstract

Differential evolution (DE) is a simple but powerful evolutionary algorithm, which has been widely and successfully used in various areas for solving complex optimization problems. In this chapter, an event-triggered impulsive control scheme (ETI) is introduced to improve the performance of DE. Impulsive control, the concept of which derives from control theory, aims at regulating the states of a network by instantly adjusting the states of a fraction of nodes at certain instants, and these instants are determined by event-triggered mechanism (ETM). After impulsive control and ETM are incorporated into DE, the search performance of the population is altered in a positive way after revising the positions of some individuals at certain moments. At the end of each generation, the impulsive control operation is triggered when the update rate of the population declines or equals to zero. In detail, inspired by the concepts of impulsive control, two types of impulses are presented within the framework of DE in this chapter: stabilizing impulses and destabilizing impulses. Stabilizing impulses help the individuals with lower rankings instantly move to a desired state determined by the individuals with better fitness values. Destabilizing impulses randomly alter the positions of inferior individuals within the range of the current population. By means of intelligently modifying the positions of a part of individuals with these two kinds of impulses, both exploitation and exploration abilities of the whole population can be meliorated.

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References

  • J. Brest, S. Greiner, B. Boskovic, M. Mernik, V. Zumer, Selfadapting control parameters in differential evolution: a comparative study on numerical benchmark problems. IEEE Trans. Evol. Comput. 10(6), 646–657 (2006)

    Article  Google Scholar 

  • Y. Cai, J. Wang, Differential evolution with neighborhood and direction information for numerical optimization. IEEE Tran. Cybern. 43(6), 2202–2215 (2013)

    Article  MathSciNet  Google Scholar 

  • S. Das, P.N. Suganthan, Differential evolution: a survey of the state-of-the-art. IEEE Trans. Evol. Comput. 15(1), 4–31 (2011)

    Article  Google Scholar 

  • S. Das, A. Abraham, A. Konar, Automatic clustering using an improved differential evolution algorithm. IEEE Trans. Syst. Man. Cybern. A: Syst. Hum. 38(1), 218–237 (2008)

    Article  Google Scholar 

  • S. Das, A. Abraham, U.K. Chakraborty, A. Konar, Differential evolution using a neighborhood-based mutation operator. IEEE Trans. Evol. Comput. 13(3), 526–553 (2009)

    Article  Google Scholar 

  • B. Dorronsoro, P. Bouvry, Improving classical and decentralized differential evolution with new mutation operator and population topologies. IEEE Trans. Evol. Comput. 15(1), 67–98 (2011)

    Article  Google Scholar 

  • M.G. Epitropakis, D.K. Tasoulis, N.G. Pavlidis, V.P. Plagianakos, M.N. Vrahatis, Enhancing differential evolution utilizing proximity-based mutation operators. IEEE Trans. Evol. Comput. 15(1), 99–119 (2011)

    Article  Google Scholar 

  • W. Gong, Z. Cai, Differential evolution with ranking-based mutation operators. IEEE Trans. Cybern. 43(6), 2066–2081 (2013)

    Article  Google Scholar 

  • W. Gong, Z. Cai, C.X. Ling, C. Li, Enhanced differential evolution with adaptive strategies for numerical optimization. IEEE Trans. Syst. Man. Cybern. B Cybern. 41(2), 397–413 (2011)

    Article  Google Scholar 

  • S.-M. Guo, C.-C. Yang, Enhancing differential evolution utilizing eigenvector-based crossover operator. IEEE Trans. Evol. Comput. 19(1), 31–49 (2015)

    Article  Google Scholar 

  • S.-M. Guo, C.-C. Yang, P.-H. Hsu, J. S.-H. Tsai, Improving differential evolution with successful-parent-selecting framework, IEEE Trans. Evol. Comput. 19(5), 717–730 (2015). https://doi.org/10.1109/TEVC.2014.2375933

  • W. Heemels, M. Donkers, A.R. Teel, Periodic event-triggered control for linear systems. IEEE Trans. Autom. Control. 58(4), 847–861 (2013)

    Article  MathSciNet  Google Scholar 

  • S. Holm, A simple sequentially rejective multiple test procedure. Scand. J. Stat. 6, 65–70 (1979)

    MathSciNet  MATH  Google Scholar 

  • S.M. Islam, S. Das, S. Ghosh, S. Roy, P.N. Suganthan, An adaptive differential evolution algorithm with novel mutation and crossover strategies for global numerical optimization. IEEE Trans. Syst. Man. Cybern. B Cybern. 42(2), 482–500 (2012)

    Article  Google Scholar 

  • P. Kaelo, M. Ali, Differential evolution algorithms using hybrid mutation. Comput. Optim. Appl. 37(2), 231–246 (2007)

    Article  MathSciNet  Google Scholar 

  • H.-K. Kim, J.-K. Chong, K.-Y. Park, D.A. Lowther, Differential evolution strategy for constrained global optimization and application to practical engineering problems. IEEE Trans. Magn. 43(4), 1565–1568 (2007)

    Article  Google Scholar 

  • J. J. Liang, B. Y. Qu, P. N. Suganthan, Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization, Computational Intelligence Laboratory, Singapore. (2013)

    Google Scholar 

  • R. Mallipeddi, P.N. Suganthan, Q.-K. Pan, M.F. Tasgetiren, Differential evolution algorithm with ensemble of parameters and mutation strategies. Appl. Soft Comput. 11(2), 1679–1696 (2011)

    Article  Google Scholar 

  • F. Neri, E. Mininno, Memetic compact differential evolution for cartesian robot control. IEEE Comput. Intell. Mag. 5(2), 54–65 (2010)

    Article  Google Scholar 

  • F. Neri, V. Tirronen, Recent advances in differential evolution: a survey and experimental analysis. Artif. Intell. Rev. 33(1–2), 61–106 (2010)

    Article  Google Scholar 

  • V. Plagianakos, D. Tasoulis, M. Vrahatis, A review of major application areas of differential evolution, in Advances in Differential Evolution (Springer, Berlin, Heidelberg, 2008), pp. 197–238

    Google Scholar 

  • K. V. Price, An introduction to differential evolution, in New Ideas in Optimization (McGraw-Hill, London, 1999), pp. 79–108

    Google Scholar 

  • A.K. Qin, V.L. Huang, P.N. Suganthan, Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans. Evol. Comput. 13(2), 398–417 (2009)

    Article  Google Scholar 

  • S. Rahnamayan, H.R. Tizhoosh, M.M. Salama, Opposition-based differential evolution. IEEE Trans. Evol. Comput. 12(1), 64–79 (2008)

    Article  Google Scholar 

  • S. Sarkar, S. Das, Multi-level image thresholding based on two-dimensional histogram and maximum tsallis entropy-a differential evolution approach. IEEE Trans. Image Process 22(12), 4788–4797 (2013)

    Article  MathSciNet  Google Scholar 

  • R. Storn, K. Price, Differential Evolution-a Simple and Efficient Adaptive Scheme for Global Optimization Over Continuous Spaces, vol 3 (ICSI, Berkeley, 1995)

    MATH  Google Scholar 

  • R. Storn, K. Price, Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11(4), 341–359 (1997)

    Article  MathSciNet  Google Scholar 

  • P. Tabuada, Event-triggered real-time scheduling of stabilizing control tasks. IEEE Trans. Autom. Control. 52(9), 1680–1685 (2007)

    Article  MathSciNet  Google Scholar 

  • R. Tanabe, A. Fukunaga, Success-history based parameter adaptation for differential evolution, in 2013 IEEE Congress on Evolutionary Computation (CEC) (IEEE, 2013), pp. 71–78

    Google Scholar 

  • Y. Tang, H. Gao, W. Zhang, J. Kurths, Leader-following consensus of a class of stochastic delayed multi-agent systems with partial mixed impulses. Automatica 53, 346–354 (2015)

    Article  MathSciNet  Google Scholar 

  • L. Tang, Y. Dong, J. Liu, Differential evolution with an individual-dependent mechanism, IEEE Trans. Evol. Comput. 19(4), 560–574 (2015) https://doi.org/10.1109/TEVC.2014.2360890

  • Y. Tang, H. Gao, J. Kurths, Robust self-triggered control of networked systems under packet dropouts, IEEE Trans. Cybern. 46(12), 3294–3305 (2016). https://doi.org/10.1109/TCYB.2015.2502619

  • M. Vasile, E. Minisci, M. Locatelli, An inflationary differential evolution algorithm for space trajectory optimization. IEEE Trans. Evol. Comput. 15(2), 267–281 (2011)

    Article  Google Scholar 

  • X. Wang, M.D. Lemmon, Event-triggering in distributed networked control systems. IEEE Trans. Autom. Control. 56(3), 586–601 (2011)

    Article  MathSciNet  Google Scholar 

  • Y. Wang, Z. Cai, Q. Zhang, Differential evolution with composite trial vector generation strategies and control parameters. IEEE Trans. Evol. Comput. 15(1), 55–66 (2011)

    Article  Google Scholar 

  • Y. Wang, Z. Cai, Q. Zhang, Enhancing the search ability of differential evolution through orthogonal crossover. Inf. Sci. 185(1), 153–177 (2012)

    Article  MathSciNet  Google Scholar 

  • J. Wang, J. Liao, Y. Zhou, Y. Cai, Differential evolution enhanced with multiobjective sorting-based mutation operators. IEEE Trans. Cybern. 44(12), 2792–2805 (2014a)

    Article  Google Scholar 

  • Y. Wang, H.-X. Li, T. Huang, L. Li, Differential evolution based on covariance matrix learning and bimodal distribution parameter setting. Appl. Soft Comput. 18, 232–247 (2014b)

    Article  Google Scholar 

  • G. Wu, R. Mallipeddi, P. Suganthan, R. Wang, H. Chen, Differential evolution with multi-population based ensemble of mutation strategies. Inf. Sci. 329, 329–345 (2016)

    Article  Google Scholar 

  • J. Zhang, A.C. Sanderson, JADE: adaptive differential evolution with optional external archive. IEEE Trans Evol Comput 13(5), 945–958 (2009)

    Article  Google Scholar 

  • W. Zhang, Y. Tang, Q. Miao, W. Du, Exponential synchronization of coupled switched neural networks with mode-dependent impulsive effects. IEEE Trans. Neural Networks Learn. Syst. 24(8), 1316–1326 (2013)

    Article  Google Scholar 

  • W. Zhang, Y. Tang, X. Wu, J.-A. Fang, Synchronization of nonlinear dynamical networks with heterogeneous impulses. IEEE Trans. Circ. Syst.-I: Regular Pap. 61(4), 1220–1228 (2014)

    Google Scholar 

  • W. Zhu, Y. Tang, J.-a. Fang, W. Zhang, Adaptive population tuning scheme for differential evolution. Inf. Sci. 223, 164–191 (2013)

    Article  Google Scholar 

  • W. Zou, D. Senthilkumar, R. Nagao, I.Z. Kiss, Y. Tang, A. Koseska, J. Duan, J. Kurths, Restoration of rhythmicity in diffusively coupled dynamical networks. Nat. Commun. 6, 7709 (2015)

    Article  Google Scholar 

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Authors and Affiliations

  1. Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, China

    Wei Du & Yang Tang

Authors
  1. Wei Du
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  2. Yang Tang
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Correspondence to Wei Du .

Editor information

Editors and Affiliations

  1. School of Computer Science, Queensland University of Technology, Brisbane, QLD, Australia

    Yu-Chu Tian

  2. School of Electrical and Information Engineering, The University of Sydney, Sydney, NSW, Australia

    David Charles Levy

Section Editor information

  1. Department of Automation, East China University of Science and Technology, Shanghai, China

    Yang Tang

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Du, W., Tang, Y. (2022). Event-Triggering Impulsive Differential Evolution. In: Tian, YC., Levy, D.C. (eds) Handbook of Real-Time Computing. Springer, Singapore. https://doi.org/10.1007/978-981-287-251-7_15

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