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Self-feedback differential evolution adapting to fitness landscape characteristics

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Abstract

Differential evolution (DE) is one of the most powerful and versatile evolutionary algorithms for efficiently solving complex real-world optimization problems in recent years. Since its introduction in 1995, the research focus in DE has mostly been on the variant side with so many new algorithms proposed based on the original DE algorithm. However, each new algorithm is only suitable for certain fitness landscapes, and, therefore, some types of optimization problems cannot be solved efficiently. To tackle this issue, this paper presents a new self-feedback DE algorithm, named the SFDE; its optimal variation strategy is selected by extracting the local fitness landscape characteristics in each generation population and combing the probability distributions of unimodality and multimodality in each local fitness landscape. The proposed algorithm is tested on a suite of 17 benchmark functions, and the experimental results demonstrated its advantages in a high search dimension in that it can ensure that the population moves to a better fitness landscape, then speeds up convergence to the global optimum, and avoids falling into local optima.

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References

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

    Article  Google Scholar 

  • Chaofeng G, Meilian L (2013) Improved differential evolution algorithm and its application in dynamic programming. J Henan Univ (Nat Sci) 43(1):79–84

    Google Scholar 

  • Cui L, Li G, Lin Q, Chen J, Lu N (2016) Adaptive differential evolution algorithm with novel mutation strategies in multiple sub-populations. Compu Oper Res 67:155–173

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  Google Scholar 

  • Davidor Y (1991) Epistasis variance: a viewpoint on GA-hardness. Found Genet Algorithms 1:23–35

    Google Scholar 

  • Hou J, Chen J, Sun S, Chen Z (2016) Adaptive mixed-hybrid and penalty discontinuous Galerkin method for two-phase flow in heterogeneous media. J Comput Appl Math 307:262–283

    Article  MathSciNet  MATH  Google Scholar 

  • Jones T, Forrest S (1995) Fitness distance correlation as a measure of problem difficulty for genetic algorithms. Santa Fe Institute. Working paper 95-02-022

  • Li Y (2016) Collision analysis and improvement of a hash function based on chaotic tent map. Optik-Int J Light Electron Opt 127(10):4484–4489

    Article  Google Scholar 

  • Li H, Zhang L (2014) A discrete hybrid differential evolution algorithm for solving integer programming problems. Engg Optim 46(9):1238–1268

    Article  MathSciNet  Google Scholar 

  • Li J, Chen X, Li M, Li J, Lee PP, Lou W (2014) Secure deduplication with efficient and reliable convergent key management. IEEE Trans Parallel Distrib Syst 25(6):1615–1625

    Article  Google Scholar 

  • Li J, Yan H, Liu Z, Chen X, Huang X, Wong DS (2017) Location-sharing systems with enhanced privacy in mobile online social networks. IEEE Syst J 11(2):439–448

    Article  Google Scholar 

  • Liu J, Lampinen J (2002) A fuzzy adaptive differential evolution algorithm. In TENCON’02. Proceedings of 2002 IEEE region 10 conference on computers, communications, control and power engineering, vol 1. IEEE, pp 606–611

  • Liu H, Cai Z, Wang Y (2010) Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization. Appl Soft Comput 10(2):629–640

  • Liu H, Zhang P, Wang K, Yang B, Chen Z (2016) Performance and scalability analysis for parallel reservoir simulations on three supercomputer architectures. In: Proceedings of the XSEDE16 conference on diversity, big data, and science at scale. ACM, p 9

  • Merz P, Freisleben B (1999) Fitness landscapes and memetic algorithm design. In: Corne D, Dorigo M, Glover F (eds) New ideas in optimization. McGraw-Hill, London, pp 245–260

    Google Scholar 

  • Nghiem L. Mirzabozorg,A. Yang,C. Chen Z (2013) Differential evolution for assisted history matching process: Sagd case study. In: SPE heavy oil conference-Canada. Society of Petroleum Engineers

  • Radcliffe NJ, Surry PD (1994) Fitness variance of formae and performance prediction. In FOGA 3:51–72

    Google Scholar 

  • Reeves CR (2014) Fitness landscapes. In: Burke EK, Kendall G (eds) Search methodologies. Springer, Boston, MA, pp 681–705

    Chapter  Google Scholar 

  • Reeves CR, Eremeev AV (2004) Statistical analysis of local search landscapes. J Oper Res Soc 55(7):687–693

    Article  MATH  Google Scholar 

  • Ronghui L, Jianguo Z (2011) Two-stage differential evolution algorithm and function optimization. J Huazhong Univ Sci Technol Nat Sci 39(11):50–55

    MathSciNet  MATH  Google Scholar 

  • Shen L, He, J (2010) A mixed strategy for evolutionary programming based on local fitness landscape. In: 2010 IEEE congress on evolutionary computation (CEC). IEEE, pp 1–8

  • 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  MATH  Google Scholar 

  • Tran DH, Cheng MY, Pham AD (2016) Using fuzzy clustering chaotic-based differential evolution to solve multiple resources leveling in the multiple projects scheduling problem. Alex Eng J 55(2):1541–1552

    Article  Google Scholar 

  • Wang X, Tang L (2016) An adaptive multi-population differential evolution algorithm for continuous multi-objective optimization. Inf Sci 348:124–141

    Article  Google Scholar 

  • Wang H, Rahnamayan S, Sun H, Omran MG (2013) Gaussian bare-bones differential evolution. IEEE Trans Cybern 43(2):634–647

    Article  Google Scholar 

  • Wang H, Wang W, Zhou X, Sun H, Zhao J, Yu X, Cui Z (2017) Firefly algorithm with neighborhood attraction. Inf Sci 382:374–387

    Article  Google Scholar 

  • Weilin W, Yonghua Z (2014) The swarm intelligence algorithm based on combination of difference evolution and cat swarm optimization algorithms. Comput Technol Autom 33(4):78–83

    Google Scholar 

  • Weinberger E (1990) Correlated and uncorrelated fitness landscapes and how to tell the difference. Biol Cybern 63(5):325–336

    Article  MATH  Google Scholar 

  • Yao X, Liu Y, Lin G (1999) Evolutionary programming made faster. IEEE Trans Evolut Comput 3(2):82–102

    Article  Google Scholar 

  • Zhao S, Hao Z, Huang H, Tan Y (2013) Multi-objective differential evolution algorithm based on adaptive mutation and partition selection. JCP 8(10):2695–2700

    Google Scholar 

  • Zhao Z, Yang J, Hu Z, Che H (2016) A differential evolution algorithm with self-adaptive strategy and control parameters based on symmetric Latin hypercube design for unconstrained optimization problems. Eur J Oper Res 250(1):30–45

    Article  MathSciNet  MATH  Google Scholar 

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant Nos. 61573157 and 61561024, the Science and Technology Planning Project of Guangdong Province with the Grant No. 2017A010101037, the Science and Technology Research Project of Jiangxi Province under Grant No. GJJ160631, and the Science Foundation of Jiangxi University of Science and Technology under the Grant No. NSFJ2015-K13.

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Authors and Affiliations

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

    Wei Li & Liang Zhong

  2. School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou, Jiangxi, China

    Wei Li & Chengtian Ouyang

  3. Southern Capital Management Co., Ltd, Shenzhen, China

    Shanni Li

  4. Department of Chemical and Petroleum Engineering, University of Calgary, Calgary, AB, Canada

    Zhangxin Chen

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  1. Wei Li
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  2. Shanni Li
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  3. Zhangxin Chen
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  4. Liang Zhong
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  5. Chengtian Ouyang
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Correspondence to Shanni Li.

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The authors declare that there is no conflict of interest regarding the publication of this paper.

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Communicated by V. Loia.

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Li, W., Li, S., Chen, Z. et al. Self-feedback differential evolution adapting to fitness landscape characteristics. Soft Comput 23, 1151–1163 (2019). https://doi.org/10.1007/s00500-017-2833-y

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