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Self-adaptive, multipopulation differential evolution in dynamic environments

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Abstract

The present work proposes a simple but effective self-adaptive strategy to control the behaviour of a differential evolution (DE) based multipopulation algorithm for dynamic environments. Specifically, the proposed scheme is aimed to control the creation of random individuals by the self-adaptation of the involved parameter. An interaction scheme between random and conventional DE individuals is also proposed and analyzed. The conducted computational experiments show that self-adaptation is profitable, leading to an algorithm that is as competitive as other efficient methods and able to beat the winner of the CEC 2009 competition on dynamic environments.

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Notes

  1. http://www.dynamic-optimization.org/.

  2. Although the name of this algorithm suggests the presence of Differential Evolution paradigm, the “DE” of its name is used to denote diversity and efficiency.

References

  • Angeline P (1995) Adaptive and self-adaptive evolutionary computations. In: Palaniswami M, Attikiouzel Y (eds) Computational intelligence: a dynamic systems perspective, IEEE Press, pp 152–163

  • Angeline P (1997) Tracking extrema in dynamic environments. In: Angeline P, Reynolds R, McDonnell J, Eberhart R (eds) Evolutionary programming VI, lecture notes in Computer Science, vol 1213. Springer, Berlin , pp 335–345

  • Bäck T (1997) Self-adaptation. In: Bäck T, Fogel D, Michalewicz Z (eds) Handbook of evolutionary computation, Oxford University Press, New York

  • Beyer HG, Deb K (2001) On self-adaptive features in real-parameter evolutionary algorithms. IEEE transactions Evol Comput 5(3):250–270

    Article  Google Scholar 

  • Blackwell T (2003) Swarms in dynamic environments. In: lecture notes in Computer Science (including subseries lecture notes in Artificial Intelligence and lecture notes in Bioinformatics), Springer, 2723:1–12

  • Blackwell T, Branke J (2006) Multiswarms, exclusion, and anti-convergence in dynamic environments. IEEE Trans Evol Comput 10(4):459–472

    Article  Google Scholar 

  • Branke J (1999) Memory enhanced evolutionary algorithms for changing optimization problems. In: Angeline PJ, Michalewicz Z, Schoenauer M, Yao X, Zalzala A (eds) Proceedings of the Congress on evolutionary computation, IEEE Press, Mayflower Hotel, Washington, vol 3, pp 1875–1882

  • Branke J (2001) Evolutionary optimization in dynamic environments. Genetic Algorithms Evol Comput, Springer, Berlin

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

    Article  Google Scholar 

  • Brest J, Boskovic B, Greiner S, Zumer V, Maucec M (2007) Performance comparison of self-adaptive and adaptive differential evolution algorithms. Soft computing—a fusion of foundations, methodologies and applications 11:617–629

    Google Scholar 

  • Brest J, Zamuda A, Boskovic B, Maucec MS, Zumer V (2009) Dynamic optimization using self-adaptive differential evolution. In: CEC09: Proceedings of the Eleventh conference on Congress on evolutionary computation, IEEE Press, Piscataway, pp 415–422

  • Büche D, Müller S, Koumoutsakos P (2003) Self-adaptation for multi-objective evolutionary algorithms. Lecture notes in computer science 2632:267–281

  • Clerc M (2006) Particle swarm optimization. Wiley—ISTE

  • Cruz C, González J, Pelta D (2011) Optimization in dynamic environments: a survey on problems, methods and measures. Soft computing—a fusion of foundations, methodologies and applications 15(7):1427–1448

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Eiben A, Michalewicz Z, Schoenauer M, Smith J (2007) Parameter control in evolutionary algorithms. In: Lobo F, Lima C, Michalewicz Z (eds) Parameter setting in evolutionary algorithms, Studies in computational intelligence, vol 54, Springer Berlin, pp 19–46

  • Olivetti de Frana F, Von Zuben FJ (2009) A dynamic artificial immune algorithm applied to challenging benchmarking problems. In: CEC09: Proceedings of the Eleventh conference on Congress on evolutionary computation, IEEE Press, pp 423–430

  • Goudos S (2009) Design of microwave broadband absorbers using a self-adaptive differential evolution algorithm. Int J RF Microw Comput Aided Eng 19(3):364–372

    Article  Google Scholar 

  • Igel C, Toussaint M (2003) Neutrality and self-adaptation. Natural Comput 2(2):117–132

    Article  MathSciNet  MATH  Google Scholar 

  • Jin Y, Branke J (2005) Evolutionary optimization in uncertain environments-a survey. IEEE Trans Evol Comput 9(3):303–317

    Article  Google Scholar 

  • Lepagnot J, Nakib A, Oulhadj H, Siarry P (2012) A dynamic multi-agent algorithm applied to challenging benchmark problems. In: Li X (ed) Proceedings of the 2012 IEEE Congress on evolutionary computation, Brisbane, Australia, pp 2621–2628

  • Li C, Yang S (2008) A generalized approach to construct benchmark problems for dynamic optimization. In: Simulated Evolution and Learning. Lecture Notes in Computer Science. Springer, Berlin 5361: 391-400

    Google Scholar 

  • Li C, Yang S (2009) A clustering particle swarm optimizer for dynamic optimization. In: CEC09 Proceedings of the Eleventh conference on Congress on evolutionary computation, IEEE Press, Piscataway, pp 439–446

  • Li C, Yang S, Nguyen TT, Yu EL, Yao X, Jin Y, Beyer HG, Suganthan PN (2008) Benchmark generator for cec2009 competition on dynamic optimization. Tech. rep., Department of Computer Science, University of Leicester, UK

  • Mendes R, Mohais AS (2005) Dynde: A differential evolution for dynamic optimization problems. In: CEC’05: Proceedings of the IEEE Congress on evolutionary computation, IEEE Press 2: 2808–2815

    Google Scholar 

  • Meyer-Nieberg S, Beyer HG (2007) Self-adaptation in evolutionary algorithms. In: Lobo F, Lima C, Michalewicz Z (eds) Parameter Setting in evolutionary algorithms, Studies in computational intelligence, vol 54, Springer, Berlin, pp 19–46

  • Morrison R (2003) Performance measurement in dynamic environments. In: Branke J (ed) GECCO Workshop on evolutionary algorithms for dynamic optimization problems

  • Morrison R, De Jong K (1999) A test problem generator for non-stationary environments. In: Proceedings of the 1999 Congress on evolutionary computation 3:2050–2053

    Google Scholar 

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

    Article  Google Scholar 

  • Nobakhti A, Wang H (2008) A simple self-adaptive differential evolution algorithm with application on the alstom gasifier. Appl Soft Comput J 8(1):350–370

    Article  Google Scholar 

  • Novoa-Hernández P, Corona C, Pelta D (2011) Efficient multi-swarm pso algorithms for dynamic environments. Memetic Comput 3:163–174

    Article  Google Scholar 

  • Pan QK, Suganthan P, Wang L, Gao L, Mallipeddi R (2011) A differential evolution algorithm with self-adapting strategy and control parameters. Comput Oper Res 38:394–408

    Article  MathSciNet  MATH  Google Scholar 

  • Pelta D, Cruz C, Verdegay J (2009) Simple control rules in a cooperative system for dynamic optimization problems. Int J Gen Syst 38(7):701–717

    Article  MATH  Google Scholar 

  • du Plessis MC, Engelbrecht AP (2011) Self-adaptive competitive differential evolution for dynamic environments. In: IEEE symposium on differential evolution—SDE

  • du Plessis MC, Engelbrecht AP (2012) Using competitive population evaluation in a differential evolution algorithm for dynamic environments. Eur J Oper Res 218(1):7–20

    Article  MathSciNet  MATH  Google Scholar 

  • Qin AK, Huang VL, Suganthan PN (2009) Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans Evol Comput 13(2):398–417

    Article  Google Scholar 

  • Schönemann L (2007) Evolution strategies in dynamic environments. In: Yang S, Ong YS, Jin Y (eds) Evolutionary computation in dynamic and uncertain environments, Studies in computational intelligence. Springer, Berlin, vol 51, pp 51–77

  • Schwefel HP (1981) Numerical optimization of computer models. John Wiley, Chichester, UK

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

    Article  MathSciNet  MATH  Google Scholar 

  • Teng N, Teo J, Hijazi M (2009) Self-adaptive population sizing for a tune-free differential evolution. Soft Comput 13(7):709–724

    Article  Google Scholar 

  • Teo J (2006) Exploring dynamic self-adaptive populations in differential evolution. Soft Comput 10(8):673–686

    Article  Google Scholar 

  • Wang YN, Wu, L-H, Yuan XF (2010) Multi-objective self-adaptive differential evolution with elitist archive and crowding entropy-based diversity measure. Soft Comput 14(3):193–209

    Google Scholar 

  • Weicker K (2002) Performance measures for dynamic environments. In: Merelo Guervós JJ, Adamidis P, Beyer HG, Fernández-Villacańas JL, Schwefel HP (eds) Parallel problem solving from nature, vol VII, pp 64–73

  • Weicker K, Weicker N (1999) On evolution strategy optimization in dynamic environments. In: Congress on Evol Comput 3:2039–2046

    Google Scholar 

  • Yang Z, Tang K, Yao X (2011) Scalability of generalized adaptive differential evolution for large-scale continuous optimization. Soft Comput 15(11):2141–2155

    Article  Google Scholar 

  • Yao X, Liu Y (1996) Fast evolutionary programming. In: Proceedings of the fifth annual conference on evolutionary programming, MIT Press, pp 451–460

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

    Article  Google Scholar 

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Acknowledgments

This work was supported in part by the projects TIN2011-27696-C02-01 from the Spanish Ministry of Economy and Competitiveness, P11-TIC-8001 from the Andalusian Government. We would like to thank the editor and the referees for their valuable comments and constructive recommendations, which helped to improve the quality and presentation of the article.

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Authors and Affiliations

  1. Department of Mathematics, University of Holguin, 80100,  Holguin, Cuba

    Pavel Novoa-Hernández

  2. Department Computer Sciences and Artificial Intelligence (CITIC-UGR), University of Granada, Granada, Spain

    Carlos Cruz Corona & David A. Pelta

Authors
  1. Pavel Novoa-Hernández
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  2. Carlos Cruz Corona
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  3. David A. Pelta
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Corresponding author

Correspondence to Carlos Cruz Corona.

Additional information

Communicated by G. Acampora.

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Novoa-Hernández, P., Corona, C.C. & Pelta, D.A. Self-adaptive, multipopulation differential evolution in dynamic environments. Soft Comput 17, 1861–1881 (2013). https://doi.org/10.1007/s00500-013-1022-x

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