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Multi-population co-genetic algorithm with double chain-like agents structure for parallel global numerical optimization

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Abstract

For the low optimization precision and long optimization time of genetic algorithm, this paper proposed a multi-population agent co-genetic algorithm with chain-like agent structure (MPAGA). This algorithm adopted multi-population parallel searching mode, close chain-like agent structure, cycle chain-like agent structure, dynamic neighborhood competition and orthogonal crossover strategy to realize parallel optimization, and has the characteristics of high optimization precision and short optimization time. In order to verify the optimization precision of this algorithm, some popular benchmark test functions were used for comparing this algorithm and a popular agent genetic algorithm (MAGA). The experimental results show that MPAGA has higher optimization precision and shorter optimization time than MAGA.

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References

  1. Goldberg DE (1989) Genetic algorithms in search optimization & machine learning. Addison-Wesley, Reading

    MATH  Google Scholar 

  2. Holland JH (1992) Adaptation in nature and artificial system. MIT Press, Cambridge

    Google Scholar 

  3. Michalewicz Z, Fogel DB (2000) How to solve it: modern heuristics. Springer, Berlin, pp. 83–234

    MATH  Google Scholar 

  4. Potvin J, Dube D, Robillard C (1966) Hybrid approach to vehicle routing using neural networks and genetic algorithm. Appl Intell 6(3), 241–252

    Article  Google Scholar 

  5. Srinivas M, Patnaik LM (1994) Adaptive probabilities of crossover and mutation in genetic algorithms. IEEE Trans Syst Man Cybern Part B 24(4), 656–667

    Article  Google Scholar 

  6. Gong D-W, Sun X-Y, Guo X-J (2002) Novel survival of the fittest genetic algorithm. Control Decis 17(6), 908–911

    Google Scholar 

  7. Zhong W, Liu J, Xue M, Jiao L (2004) A multi-agent genetic algorithm for global numerical optimization. IEEE Trans Syst Man Cybern Part B 34(2), 1128–1141

    Article  Google Scholar 

  8. Zeng X-P, Li Y-M, Qin J A dynamic chain-like agent genetic algorithm for global numerical optimization and feature selection, Neurocomputing. Available online at www.sciencedirect.com

  9. Olsson B (2001) Co-evolutionary search in asymmetric spaces. Inf Sci 133(3–4), 103–125

    Article  MATH  Google Scholar 

  10. Potter MA, De Jong KA (2000) Cooperative coevolution: an architecture for evolving coadapted subcomponents. IEEE Trans Evol Comput 8(1), 1–29

    Article  Google Scholar 

  11. Gao Y, Xie S-L, Xu R-N, Li Z-H (2006) A multi sub-population particle swarm optimizer based on clustering. Appl Res Comput 23(4), 40–41

    Google Scholar 

  12. Su C-H, Hou T-H (2008) Using multi-population intelligent genetic algorithm to find the Pareto-optimal parameters for a nano-particle milling process. Expert Syst Appl 34(4), 2502–2510

    Article  Google Scholar 

  13. Leung YW, Wang Y (2001) An orthogonal genetic algorithm with quantization for global numerical optimization. IEEE Trans Evol Comput 5(2), 41–53

    Article  Google Scholar 

  14. Pan ZJ, Kang LS (1997) An adaptive evolutionary algorithms for numerical optimization. In: X Yao, JH Kim, T Furuhashi (eds.) Simulated evolution and learning. Lecture notes in artificial intelligence. Springer, Berlin, pp 27–34

    Chapter  Google Scholar 

  15. Yao X, Liu Y, Lin G (1999) Evolutionary programming made faster. IEEE Trans Evol Comput 3(7), 82–102

    Google Scholar 

  16. Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1), 67–82

    Article  Google Scholar 

  17. Whitley D (1999) A Free Lunch proof for gray versus binary encodings. In: W Banzhaf, J Daida, AE Eiben, MH Garzon, V Honavar, M Jakiela, RE Smith (eds.), Proceedings of the genetic and evolutionary computation conference (GECCO’99), Orlando, FL, vol 1. Morgan Kaufmann, San Francisco, pp 726–733

    Google Scholar 

  18. Kimbrough SO, Koehler GJ, Lu M, Wood DH On a Feasible–Infeasible Two-Population (FI-2Pop) genetic algorithm for constrained optimization: Distance tracing and no free lunch. European J Oper Res. Available online at www.sciencedirect.com

  19. Schumacher C, Vose MD, Whitley LD (2001) The No Free Lunch and description length. In: L Spector, E Goodman, A Wu, W Langdon, H-M Voigt, M Gen, S Sen, M Dorigo, S Pezeshk, M Garzon, E Burke (eds.), Proceedings of the genetic and evolutionary computation conference (GECCO 2001), San Francisco, CA. Morgan Kaufmann, San Francisco, pp 565–570

    Google Scholar 

  20. Tu ZG, Lu Y (2004) A robust stochastic genetic algorithms (StGA) for global numerical optimization. IEEE Trans Evol Comput 8(5), 456–470

    Article  Google Scholar 

  21. Koehler GJ (2007) Conditions that obviate the no free lunch theorems for optimization. Inf J Comput 19(2), 273–279

    Article  MathSciNet  Google Scholar 

  22. Igel C, Toussaint M (2003) On classes of functions for which no free lunch results hold. Inf Process Lett 86(6), 317–321

    Article  MATH  MathSciNet  Google Scholar 

  23. Deb K, Pratap A, Agarwal S, Meyarivan TA (2002) A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2), 182–197

    Article  Google Scholar 

  24. Mitchell M (1996) An introduction to genetic algorithms. MIT Press, Cambridge, pp 155–181

    Google Scholar 

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Authors and Affiliations

  1. College of Communication Engineering, Chongqing University, Chongqing, 400030, China

    Yongming Li & Xiaoping Zeng

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  1. Yongming Li
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  2. Xiaoping Zeng
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Correspondence to Yongming Li.

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Li, Y., Zeng, X. Multi-population co-genetic algorithm with double chain-like agents structure for parallel global numerical optimization. Appl Intell 32, 292–310 (2010). https://doi.org/10.1007/s10489-008-0146-7

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  • DOI: https://doi.org/10.1007/s10489-008-0146-7

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