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Global replacement-based differential evolution with neighbor-based memory for dynamic optimization

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Abstract

Dynamic optimization problems challenge the evolutionary algorithms, owing to the diversity loss or the low search efficiency of the algorithms, especially when the problems change frequently. This paper presents a novel differential evolution algorithm to address the dynamic optimization problems. Unlike the most used “DE/rand/1” mutation operator, in this paper, the “DE/best/1” mutation is employed to generate a mutant individual. In order to enhance the search efficiency of differential evolution, the classical differential evolution algorithm is modified by a novel replacement operator, in which the worst individual in the whole population is replaced by the newly generated trial vector as a “steady-state” manner. During optimizing, some newly generated solutions are stored into a memory set, in which these stored solutions are located around the current best solution. When the environmental change is detected, the stored solutions are expected to guide the reinitialized solutions to track the new location of global optimum as soon as possible. The performance of the proposed algorithm is compared with six state-of-the-art dynamic evolutionary algorithms over some benchmark problems. The experimental results show that the proposed algorithm clearly outperforms the competitors.

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References

  1. Nguyen TT, Yang S, Branke J (2012) Evolutionary dynamic optimization: A survey of the state of the art. Swarm Evol Comput 6:1–24

    Article  Google Scholar 

  2. Cruz C, Gonzalez JR, Pelta DA (2011) Optimization in dynamic environments: A survey on problems, methods and measures. Soft Comput 15:1427–1448

    Article  Google Scholar 

  3. Yang S, Cheng H, Wang F (2010) Genetic algorithms with immigrants and memory schemes for dynamic shortest path routing problems in mobile ad hoc networks. IEEE Trans Syst Man, Cybern C Appl Rev 40:52–63

    Article  Google Scholar 

  4. Mavrovouniotis M, Yang S (2015) Ant algorithms with immigrants schemes for the dynamic vehicle routing problem. Inf Sci (Ny) 294:456–477

    Article  MathSciNet  Google Scholar 

  5. Zhu Z, Gao X, Cao L et al (2015) Research on the shift strategy of HMCVT based on the physical parameters and shift time. Appl Math Model. https://doi.org/10.1016/j.apm.2016.02.017

  6. Mavrovouniotis M, Li C, Yang S (2017) A survey of swarm intelligence for dynamic optimization: Algorithms and applications. Swarm Evol Comput 33:1–17

    Article  Google Scholar 

  7. Das S, Suganthan PN (2011) Differential Evolution?: A Survey of the art. IEEE Trans Evol Comput 15:4–31

    Article  Google Scholar 

  8. Mendes R, Mohais AS (2005) DynDE: a differential evolution for dynamic optimization problems. In: Proceeding IEEE Congr. Evol. Comput. pp 2808–2815

  9. Halder U, Das S, Maity D (2013) A cluster-based differential evolution algorithm with external archive for optimization in dynamic environments. IEEE Trans Cybern 43:881–897

    Article  Google Scholar 

  10. Grefenstette JJ (1992) Genetic algorithms for changing environments. In: Parallel Probl. Solving from Nature, PPSN, pp 137–144

  11. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: 1995 IEEE Int. Conf. Neural Networks. pp 1942–1948

  12. Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm. J Glob Optim 39:459–471

    Article  MathSciNet  MATH  Google Scholar 

  13. Rezaee Jordehi A (2014) Particle swarm optimisation for dynamic optimisation problems: a review. Neural Comput Appl 25:1507–1516

    Article  Google Scholar 

  14. Li C, Nguyen TT, Yang M et al (2015) Multi-population methods in unconstrained continuous dynamic environments: The challenges. Inf Sci (Ny) 296:95–118

    Article  Google Scholar 

  15. Yang S, Li C (2010) A clustering particle swarm optimizer for locating and tracking multiple optima in dynamic environments. IEEE Trans Evol Comput 14:959–974

    Article  Google Scholar 

  16. Li C, Yang S (2009) A clustering particle swarm optimizer for dynamic optimization. IEEE Congr Evol Comput 439–446

  17. Parrott D, Li Xiaodong (2004) A particle swarm model for tracking multiple peaks in a dynamic environment using speciation. In: Proc. IEEE Congr. Evol. Comput. pp 98–103

  18. Luo W, Sun J, Bu C, Liang H (2016) Species-based Particle Swarm Optimizer enhanced by memory for dynamic optimization. Appl Soft Comput 47:130–140

    Article  Google Scholar 

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

    Article  Google Scholar 

  20. Li C, Yang S (2008) Fast multi-swarm optimization for dynamic optimization problems. Fourth Int Conf Nat Comput 7:624–628

    Google Scholar 

  21. Du Plessis MC, Engelbrecht AP (2013) Differential evolution for dynamic environments with unknown numbers of optima. J Glob Optim 55:73–99

    Article  MathSciNet  MATH  Google Scholar 

  22. Blackwell T, Branke J (2004) Multi-swarm optimization in dynamic environments. In: Appl. Evol. Comput. pp 489–500

  23. Liu L, Yang S, Wang D (2010) Particle swarm optimization with composite particles in dynamic environments. Syst Man, Cybern Part B Cybern IEEE Trans 40:1634–1648

    Article  Google Scholar 

  24. NIchabadi A, Ebadzadeh M., Safabakhsh R (2012) A competitive clustering particle swarm optimizer for dynamic optimization problems. Swarm Intell 6:177–206

    Article  Google Scholar 

  25. Karimi J, Nobahari H, Pourtakdoust SH (2012) A new hybrid approach for dynamic continuous optimization problems. Appl Soft Comput 12:1158–1167

    Article  Google Scholar 

  26. Brest J, Zamuda A, Boskovic B et al (2009) Dynamic optimization using self-adaptive differential evolution. In: Proc. IEEE Congr. Evol. Comput. pp 415–422

  27. Das S, Mandal A, Mukherjee R (2013) An adaptive differential evolution algorithm for global optimization in dynamic environments. IEEE Trans Cybern 44:966–978

    Article  Google Scholar 

  28. Mukherjee R, Patra GR, Kundu R, Das S (2014) Cluster-based differential evolution with Crowding Archive for niching in dynamic environments. Inf Sci (Ny) 267:58–82

    Article  MathSciNet  Google Scholar 

  29. Mukherjee R, Debchoudhury S, Das S (2016) Modified differential evolution with locality induced genetic operators for dynamic optimization. Eur J Oper Res 253:337–355

    Article  MATH  Google Scholar 

  30. Lung RI, Dumitrescu D (2007) A collaborative model for tracking optima in dynamic environments. In: Proc. IEEE Congr. Evol. Comput. pp 564–567

  31. Zuo X, Xiao L (2014) A DE and PSO based hybrid algorithm for dynamic optimization problems. Soft Comput 18:1405–1424

    Article  Google Scholar 

  32. Kordestani Kazemi J, Rezvanian A, Meybodi Reza M (2014) CDEPSO?: a bi-population hybrid approach for dynamic optimization problems. Appl Intell 40:682–694

    Article  Google Scholar 

  33. Boulesnane A, Meshoul S (2017) WD2O?: a novel wind driven dynamic optimization approach with effective change detection. Appl Intell 47:488–504

    Article  Google Scholar 

  34. Kordestani JK, Firouzjaee HA, Meybodi MR (2017) An adaptive bi-flight cuckoo search with variable nests for continuous dynamic optimization problems. Appl Intell. https://doi.org/10.1007/s10489-017-0963-7

  35. Li C, Yang S (2012) A general framework of multipopulation methods with clustering in undetectable dynamic environments. IEEE Trans Evol Comput 16:556–577

    Article  Google Scholar 

  36. Li Changhe, Yang Shengxiang YM (2014) An adaptive multi-swarm optimizer for dynamic optimization problems. Evol Comput 22:559–594

    Article  Google Scholar 

  37. Blackwell TM, Branke J, Li X (2008) Particle Swarms for Dynamic Optimization Problems. Swarm Intell 52:193–217

    Article  Google Scholar 

  38. Cao L, Xu L, Goodman ED (2016) A guiding evolutionary algorithm with greedy strategy for global optimization problems. Comput Intell Neurosci. https://doi.org/10.1155/2016/2565809

  39. Yang S, Yao X (2008) Population-based incremental learning with associative memory for dynamic environments. IEEE Trans Evol Comput 12:542–561

    Article  Google Scholar 

  40. Hu X, Eberhart RC (2002) Adaptive particle swarm optimization: detection and response to dynamic systems. Proc 2002 Congr Evol Comput 2:1666–1670

    Google Scholar 

  41. Branke J (1999) Memory enhanced evolutionary algorithms for changing optimization problems. In: Proc. IEEE CEC. pp 1875–1882

  42. Moser I, Chiong R (2013) Dynamic function Optimization: the moving peaks benchmark. In: Metaheuristics Dyn. Optim. pp 35–59

  43. Li C, Yang S, Nguyen TT, Yu EL, Yao X, Jin Y, Beyer HG, Suganthan PN (2008) Benchmark generator for CEC 2009 competition on dynamic optimization university of leicester, University of Birmingham, Nanyang Technological University, Tech. Rep

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Funding

This work was supported by the National key research and development plan, key equipment for intelligent agricultural machinery (2016YFD0700400).

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Authors and Affiliations

  1. Automotive Engineering Research Institute, Jiangsu University, Zhenjiang, 212013, China

    Zhen Zhu, Long Chen & Chaochun Yuan

  2. School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang, 212013, China

    Changgao Xia

Authors
  1. Zhen Zhu
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  2. Long Chen
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  3. Chaochun Yuan
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  4. Changgao Xia
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Correspondence to Long Chen.

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Zhu, Z., Chen, L., Yuan, C. et al. Global replacement-based differential evolution with neighbor-based memory for dynamic optimization. Appl Intell 48, 3280–3294 (2018). https://doi.org/10.1007/s10489-018-1147-9

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