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A novel mutation strategy selection mechanism for differential evolution based on local fitness landscape

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Abstract

The performance of differential evolution (DE) algorithm highly depends on the selection of mutation strategy. However, there are six commonly used mutation strategies in DE. Therefore, it is a challenging task to choose an appropriate mutation strategy for a specific optimization problem. For a better tackle this problem, in this paper, a novel DE algorithm based on local fitness landscape called LFLDE is proposed, in which the local fitness landscape information of the problem is investigated to guide the selection of the mutation strategy for each given problem at each generation. In addition, a novel control parameter adaptive mechanism is used to improve the proposed algorithm. In the experiments, a total of 29 test functions originated from CEC2017 single-objective test function suite which are utilized to evaluate the performance of the proposed algorithm. The Wilcoxon rank-sum test and Friedman rank test results reveal that the performance of the proposed algorithm is better than the other five representative DE algorithms.

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References

  1. Bansal S (2019) A comparative study of nature-inspired metaheuristic algorithms in search of near-to-optimal Golomb rulers for the FWM crosstalk elimination in WDM systems. Appl Artif Intell 33(14):1199–1265

    Article  Google Scholar 

  2. Di Serafino D, Ruggiero V, Toraldo G et al (2018) On the steplength selection in gradient methods for unconstrained optimization. Appl Math Comput 318:176–195

    MathSciNet  MATH  Google Scholar 

  3. Jahani E, Chizari M (2018) Tackling global optimization problems with a novel algorithm–Mouth Brooding Fish algorithm. Appl Soft Comput 62:987–1002

    Article  Google Scholar 

  4. Li K, Chen R, Fu G et al (2018) Two-archive evolutionary algorithm for constrained multiobjective optimization. IEEE Trans Evol Comput 23(2):303–315

    Article  Google Scholar 

  5. Das S, Mullick SS, Suganthan PN et al (2016) Recent advances in differential evolution—an updated survey. Swarm Evol Comput 27:1–30

    Article  Google Scholar 

  6. Das S, Mullick SS, Suganthan PN (2016) Recent advances in differential evolution–an updated survey. Swarm Evol Comput 27:1–30

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  8. Mohamed AW, Hadi AA, Jambi KM (2019) Novel mutation strategy for enhancing SHADE and LSHADE algorithms for global numerical optimization. Swarm Evol Comput 50:100455

    Article  Google Scholar 

  9. 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 

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

    Article  Google Scholar 

  11. Islam SM, Das S, Ghosh S et al (2012) An adaptive differential evolution algorithm with novel mutation and crossover strategies for global numerical optimization. IEEE Syst Man Cybern 42(2):482–500

    Article  Google Scholar 

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

    Article  Google Scholar 

  13. Brest J, Greiner S, Boskovic B et al (2006) Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems. IEEE Trans Evol Comput 10(6):646–657

    Article  Google Scholar 

  14. Tanabe R, Fukunaga A (2013) Evaluating the performance of SHADE on CEC 2013 benchmark problems. In: 2013 IEEE Congress on Evolutionary Computation. IEEE, pp 1952–1959

  15. Tanabe R, Fukunaga AS (2014) Improving the search performance of SHADE using linear population size reduction. In: 2014 IEEE Congress on Evolutionary Computation (CEC). IEEE, pp 1658–1665

  16. Brest J, Maučec MS, Bošković B (2017) Single objective real-parameter optimization: algorithm jSO. In: 2017 IEEE Congress on Evolutionary Computation (CEC). IEEE, pp 1311–1318

  17. Poláková R, Tvrdík J, Bujok P (2016) Evaluating the performance of L-SHADE with competing strategies on CEC2014 single parameter-operator test suite. In: 2016 IEEE Congress on Evolutionary Computation (CEC). IEEE, pp 1181–1187

  18. Awad NH, Ali MZ, Suganthan PN et al (2016) An ensemble sinusoidal parameter adaptation incorporated with L-SHADE for solving CEC2014 benchmark problems. In: 2016 IEEE Congress on Evolutionary Computation (CEC). IEEE, pp 2958–2965

  19. Stanovov V, Akhmedova S, Semenkin E (2018) LSHADE algorithm with rank-based selective pressure strategy for solving CEC 2017 benchmark problems. In: 2018 IEEE Congress on Evolutionary Computation (CEC). IEEE, pp 1–8

  20. Qin AK, Huang VL, Suganthan PN (2008) Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans Evol Comput 13(2):398–417

    Article  Google Scholar 

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

    Article  Google Scholar 

  22. Elsayed SM, Sarker RA, Essam DL (2011) Multi-operator based evolutionary algorithms for solving constrained optimization problem. Comput Oper Res 38(12):1877–1896

    Article  MathSciNet  Google Scholar 

  23. Mallipeddi R, Suganthan PN, Pan QK et al (2011) Differential evolution algorithm with ensemble of parameters and mutation strategies. Appl Soft Comput 11(2):1679–1696

    Article  Google Scholar 

  24. Malan KM, Engelbrecht AP (2013) A survey of techniques for characterising fitness landscapes and some possible ways forward. Inf Sci 241:148–163

    Article  Google Scholar 

  25. Wang M, Li B, Zhang G et al (2018) Population evolvability: dynamic fitness landscape analysis for population-based metaheuristic algorithms. IEEE Trans Evol Comput 22(4):550–563

    Article  Google Scholar 

  26. Li K, Liang Z, Yang S et al (2019) Performance analyses of differential evolution algorithm based on dynamic fitness landscape. Int J Cogn Inf Nat Intell (IJCINI) 13(1):36–61

    Article  Google Scholar 

  27. Malan KM, Engelbrecht AP (2009) Quantifying ruggedness of continuous landscapes using entropy. In: Congress on Evolutionary Computation, pp 1440–1447

  28. Sallam KM, Elsayed SM, Sarker RA et al (2017) Landscape-based adaptive operator selection mechanism for differential evolution. Inf Sci 418–419:383–404

    Article  Google Scholar 

  29. Viktorin A, Senkerik R, Pluhacek M et al (2019) Distance based parameter adaptation for success-history based differential evolution. Swarm Evol Comput 50:100462

    Article  Google Scholar 

  30. Tong L, Dong M, Jing C et al (2018) An improved multi-population ensemble differential evolution. Neurocomputing 290:130–147

    Article  Google Scholar 

  31. Wu G, Mallipeddi R, Suganthan PN et al (2016) Differential evolution with multi-population based ensemble of mutation strategies. Inf Sci 329:329–345

    Article  Google Scholar 

  32. Bansal S, Gupta N, Singh AK Application of bat-inspired computing algorithm and its variants in search of near-optimal golomb rulers for WDM systems: a comparative study. In: Applications of Bat Algorithm and its Variants. Springer, Singapore, pp 79–101

  33. Awad NH, Ali MZ, Mallipeddi R et al (2018) An improved differential evolution algorithm using efficient adapted surrogate model for numerical optimization. Inf Sci 451–452:326–347

    Article  MathSciNet  Google Scholar 

  34. Biedrzycki R, Arabas J, Jagodzinski D et al (2019) Bound constraints handling in differential evolution: an experimental study. Swarm Evol Comput 50:100453

    Article  Google Scholar 

  35. Derrac J, García S, Molina D et al (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol Comput 1(1):3–18

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported by National Key R&D Program of China with the Grant No.2018YFC0831100, the National Natural Science Foundation of China with the Grant No.61773296, the National Natural Science Foundation Youth Fund Project of China under Grant No. 61703170, the Major Science and Technology Project in Dongguan with Grant No. 2018215121005 and Key R&D Program of Guangdong Province with No. 2019B020219003. Besides, the authors extend their appreciation to the Deanship of Scientific Research at King Saud University for funding this work through Research Group no. RG-144-331.

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Authors and Affiliations

  1. College of Mathematics and Informatics, South China Agricultural University, Guangzhou, 510642, China

    Zhiping Tan & Kangshun Li

  2. School of Computer Engineering, Nanjing Institute of Technology, Nanjing, 210024, China

    Yuan Tian

  3. Computer Science, King Saud University, Riyadh, Kingdom of Saudi Arabia

    Najla Al-Nabhan

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  1. Zhiping Tan
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  2. Kangshun Li
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  3. Yuan Tian
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  4. Najla Al-Nabhan
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Correspondence to Kangshun Li.

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Tan, Z., Li, K., Tian, Y. et al. A novel mutation strategy selection mechanism for differential evolution based on local fitness landscape. J Supercomput 77, 5726–5756 (2021). https://doi.org/10.1007/s11227-020-03482-w

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