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Arithmetic and parent-centric headless chicken crossover operators for dynamic particle swarm optimization algorithms

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Abstract

This paper conducts an analysis of various strategies for incorporating the headless chicken macromutation operator into a dynamic particle swarm optimization algorithm. Seven variations of the dynamic headless chicken guaranteed convergence particle swarm optimization algorithm are proposed and evaluated on a diverse set of single-objective dynamic benchmark problems. Competitive performance was demonstrated by the headless chicken PSO algorithms when compared to, amongst others, a quantum particle swarm optimization algorithm.

Keywords

Headless chicken macromutation operator Particle swarm optimization Dynamic optimization Quantum particle swarm optimization 

Notes

Acknowledgements

This work is based on the research supported by the National Research Foundation (NRF) of South Africa (Grant Number 46712). The opinions, findings and conclusions or recommendations expressed in this article are that of the authors alone, and not that of the NRF. The NRF accepts no liability whatsoever in this regard.

Compliance with ethical standards

Conflict of interest

No other potential conflicts of interest exist, and the article does not contain any studies with human or animal participants. Sub Elements in Acknowledgement

References

  1. Angeline P (1997) Subtree crossover: building block engine or macromutation. Genet Program 97:9–17Google Scholar
  2. Benson K (2000) Evolving finite state machines with embedded genetic programming for automatic target detection. In: Congress on evolutionary computation, pp 1543–1549Google Scholar
  3. Blackwell TM, Bentley PJ (2002) Dynamic search with charged swarms. In: Proceedings of the 4th annual conference on genetic and evolutionary computation, Morgan Kaufmann Publishers Inc, pp 19–26Google Scholar
  4. Blackwell T, Branke J (2004) Multi-swarm optimization in dynamic environments. In: Raidl GR et al (eds) Applications of evolutionary computing. EvoWorkshops 2004. Lecture Notes in Computer Science, vol 3005. Springer, Berlin, HeidelbergGoogle Scholar
  5. Blackwell T, Branke J (2006) Multiswarms, exclusion, and anti-convergence in dynamic environments. IEEE Trans Evol Comput 10(4):459–472CrossRefGoogle Scholar
  6. Branke J (1999) Memory enhanced evolutionary algorithms for changing optimization problems. In: Proceedings of the 1999 congress on evolutionary computation, CEC 99, IEEE, vol 3, pp 1875–1882Google Scholar
  7. Branke J (2001) Evolutionary approaches to dynamic environments—updated survey. In: Proceedings of GECCO Workshop in evolutionary algorithms for dynamic optimization problems, pp 27–30Google Scholar
  8. Citi L, Poli R, Cinel C, Sepulveda F (2008) P300-based bci mouse with genetically-optimized analogue control. IEEE Trans Neural Syst Rehabil Eng 16(1):51–61CrossRefGoogle Scholar
  9. Deb K, Joshi D, Anand A (2002) Real-coded evolutionary algorithms with parent-centric recombination. In: Proceedings of the 2002 congress on evolutionary computation, CEC’02, IEEE, vol 1, pp 61–66Google Scholar
  10. Duhain JG, Engelbrecht AP (2012) Towards a more complete classification system for dynamically changing environments. In: 2012 IEEE congress on evolutionary computation (CEC), IEEE, pp 1–8Google Scholar
  11. Eberhart R, Kennedy J (1995) Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, pp 1942–1948Google Scholar
  12. Eberhart RC, Shi Y (2001) Tracking and optimizing dynamic systems with particle swarms. In: Proceedings of the 2001 congress on evolutionary computation, IEEE, vol 1, pp 94–100Google Scholar
  13. Engelbrecht AP (2006) Fundamentals of computational swarm intelligence. Wiley, New YorkGoogle Scholar
  14. Grobler J, Engelbrecht AP (2016) Headless chicken particle swarm optimization algorithms. Lecture notes in computer science: advances in swarm intelligence (ICSI 2016), vol 9712, pp 350–357Google Scholar
  15. Helbig M, Engelbrecht A (2016) Using headless chicken crossover for local guide selection when solving dynamic multi-objective optimization. In: Advances in nature and biologically inspired computing, Springer, Berlin, pp 381–392Google Scholar
  16. Hu X, Eberhart R (2001) Tracking dynamic systems with pso: whereas the cheese. In: Proceedings of the workshop on particle swarm optimization, pp 80–83Google Scholar
  17. Hu X, Eberhart R.C (2002) Adaptive particle swarm optimization: detection and response to dynamic systems. In: Proceedings of the 2002 congress on evolutionary computation, CEC’02, IEEE, vol 2, pp 1666–1670Google Scholar
  18. Hynek J (2004) Evolving strategy for game playing. In: 4th international ICSC symposium on engineering intelligent systems, pp 1–6Google Scholar
  19. Jones T (1995) Crossover, macromutation, and population-based search. In: International conference on genetic algorithms, pp 73–80Google Scholar
  20. Kennedy J, Mendes R (2002) Population structure and particle swarm performance. Proc IEEE Congr Evolut Comput 2:1671–1676Google Scholar
  21. Li C, Mavrovouniotis M, Yang S, Yao X (2013) Benchmark generator for the IEEE WCCI-2014 competition on evolutionary computation for dynamic optimization problems: dynamic rotation peak benchmark generator (DRPBG) and dynamic composition benchmark generator (DCBG). De Montfort University, UK, technical reportGoogle Scholar
  22. Li X, Branke J, Blackwell T (2006) Particle swarm with speciation and adaptation in a dynamic environment. In: Proceedings of the 8th annual conference on Genetic and evolutionary computation, ACM, pp 51–58Google Scholar
  23. Liu L, Yang S, Wang D (2010) Particle swarm optimization with composite particles in dynamic environments. IEEE Trans Syst Man Cybern Part B (Cybern) 40(6):1634–1648CrossRefGoogle Scholar
  24. Lung RI, Dumitrescu D (2007) A collaborative model for tracking optima in dynamic environments. In: IEEE congress on evolutionary computation, CEC 2007, IEEE, pp 564–567Google Scholar
  25. Michalewicz Z (1996) Genetic algorithms + data structures = evolutionary programs. Springer, BerlinCrossRefMATHGoogle Scholar
  26. Poli R, McPhee N (2000) Exact GP schema theory for headless chicken crossover with subtree mutation. Cognitive science research papers—University of Birmingham CSRPGoogle Scholar
  27. Psaraftis HN (1995) Dynamic vehicle routing: status and prospects. Ann Oper Res 61(1):143–164CrossRefMATHGoogle Scholar
  28. Van den Bergh F, Engelbrecht A (2002) A new locally convergent particle swarm optimiser. Proc Man Cybern Int Conf Syst 3:6–12CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.Department of Industrial and Systems EngineeringUniversity of PretoriaPretoriaSouth Africa
  2. 2.Department of Computer ScienceUniversity of PretoriaPretoriaSouth Africa

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