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Uniform distribution driven adaptive differential evolution

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Abstract

Evolutionary algorithms are popular optimization tools for real-world applications due to their numerous advantages such as capability of parallel search along multiple directions by maintaining a population of candidates, invariance to certain mathematical properties (convexity, continuity and hardness) of fitness landscape and ability to handle black-box problems. However, most of the current evolutionary algorithms are loosely based on heuristics inspired by nature and lack the crucial theoretical background. Motivated by the overwhelming advantages of such optimization algorithms and the necessity for theoretical foundation, this paper presents a new evolutionary algorithm - UDE (Uniform Differential Evolution) for solving single- objective optimization problems along with a theoretical analysis of the proposed UDE algorithm. Thus, this paper formally gives insights about the features and properties of the various optimization strategies used. This method is different from traditional Differential Evolution variants as it employs a uniform probability distribution for generating new candidate solutions. UDE is further developed to obtain an adaptive evolutionary algorithm - Adaptive UDE (AUDE), which has shown to obtain significant improvements in the performance and convergence speeds compared to other algorithms on a benchmark set of 19 test problems. The source codes are available at http://worksupplements.droppages.com/ude_aude.

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Notes

  1. Some illustrations are also presented at http://worksupplements.droppages.com/ude_aude.

  2. The author-implemented version of LSHADE-cn EpSin in Python 3.4 as per the specifications of [24] is also provided in http://worksupplements.droppages.com/ude_aude.

References

  1. Eftimov T, Korošec P (2019) A novel statistical approach for comparing meta-heuristic stochastic optimization algorithms according to the distribution of solutions in the search space. Inform Sci 489:255–273. https://doi.org/10.1016/j.ins.2019.03.049

    MathSciNet  Google Scholar 

  2. Wu G, Pedrycz W, Suganthan P, Li H (2017) Using variable reduction strategy to accelerate evolutionary optimization. Appl Soft Comput 61:283–293. https://doi.org/10.1016/j.asoc.2017.08.012. http://www.sciencedirect.com/science/article/pii/S1568494617304945http://www.sciencedirect.com/science/article/pii/S1568494617304945

    Google Scholar 

  3. Dasgupta S, Das S, Biswas A, Abraham A (2009) On stability and convergence of the population-dynamics in differential evolution. AI Commun 22(1):1–20

    MathSciNet  MATH  Google Scholar 

  4. Pal M, Bandyopadhyay S (2016) Many-objective feature selection for motor imagery EEG signals using differential evolution and support vector machine. In: 2016 International conference on microelectronics, computing and communications (MicroCom). IEEE, pp 1–6

  5. Zhou H, Song M, Pedrycz W (2018) A comparative study of improved GA and PSO in solving multiple traveling salesmen problem. Appl Soft Comput 64:564–580. https://doi.org/10.1016/j.asoc.2017.12.031

    Google Scholar 

  6. Abualigah LMQ, Hanandeh ES (2015) Applying genetic algorithms to information retrieval using vector space model. Int J Comput Sci Eng Appl 5(1):19–28

    Google Scholar 

  7. Gong M, Wu Y, Cai Q, Ma W, Qin A, Wang Z, Jiao L (2016) Discrete particle swarm optimization for high-order graph matching. Inform Sci 328:158–171. https://doi.org/10.1016/j.ins.2015.08.038. http://www.sciencedirect.com/science/article/pii/S0020025515006271

    Google Scholar 

  8. Abualigah LM, Khader AT (2017) Unsupervised text feature selection technique based on hybrid particle swarm optimization algorithm with genetic operators for the text clustering. J Supercomput 73(11):4773–4795

    Google Scholar 

  9. Salgotra R, Singh U, Saha S (2018) Improved cuckoo search with better search capabilities for solving CEC2017 benchmark problems. In: 2018 IEEE Congress on evolutionary computation (CEC). IEEE, pp 1–7

  10. Dorigo M, Stützle T (2019) Ant colony optimization: overview and recent advances. In: Handbook of Metaheuristics. Springer, Cham, pp 311–351

  11. Zheng F, Zecchin AC, Newman JP, Maier HR, Dandy GC (2017) An adaptive convergence-trajectory controlled ant colony optimization algorithm with application to water distribution system design problems. IEEE Trans Evol Comput 21(5):773–791. https://doi.org/10.1109/TEVC.2017.2682899

    Article  Google Scholar 

  12. Xue Y, Jiang J, Zhao B, Ma T (2018) A self-adaptive artificial bee colony algorithm based on global best for global optimization. Soft Comput 22(9):2935–2952

    Google Scholar 

  13. Camarena O, Cuevas E, Pérez-Cisneros M, Fausto F, González A, Valdivia A (2018) Ls-II: an improved locust search algorithm for solving optimization problems. Mathematical problems in engineering

  14. Singh N, Singh S (2017) A modified mean gray wolf optimization approach for benchmark and biomedical problems. Evol Bioinform 13:1176934317729413

    Google Scholar 

  15. Zhou Y, Zhou Y, Luo Q, Abdel-basset M (2017) A simplex method-based social spider optimization algorithm for clustering analysis. Eng Appl Artif Intell 64:67–82

    Google Scholar 

  16. Abualigah LMQ (2019) Feature selection and enhanced krill herd algorithm for text document clustering. Springer, Berlin, pp 1–165

    Google Scholar 

  17. Elsayed S, Sarker R, Slay J (2015) Evaluating the performance of a differential evolution algorithm in anomaly detection. In: 2015 IEEE congress on evolutionary computation (CEC). IEEE, pp 2490–2497

  18. Hou Y, Zhao L, Lu H (2018) Fuzzy neural network optimization and network traffic forecasting based on improved differential evolution. Fut Gen Comput Syst 81:425–432

    Google Scholar 

  19. Liu J (2005) A fuzzy adaptive differential evolution algorithm. Soft Comput 9(6):448–462

    MATH  Google Scholar 

  20. 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 Evol Comput 10 (6):646–657

    Google Scholar 

  21. Qin AK, Suganthan PN (2005) Self-adaptive differential evolution algorithm for numerical optimization. In: 2005 IEEE congress on evolutionary computation. https://doi.org/10.1109/CEC.2005.1554904, vol 2. IEEE, pp 1785–1791

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

    Google Scholar 

  23. Sengupta R, Pal M, Saha S, Bandyopadhyay S (2019) Population dynamics indicators for evolutionary many-objective optimization. In: Progress in advanced computing and intelligent engineering. Springer, Singapore, pp 261–271

  24. Awad NH, Ali MZ, Suganthan PN (2017) Ensemble sinusoidal differential covariance matrix adaptation with Euclidean neighborhood for solving CEC2017 benchmark problems. In: 2017 IEEE congress on evolutionary computation (CEC). https://doi.org/10.1109/CEC.2017.7969336. IEEE, pp 372–379

  25. Deb K, Deb D (2014) Analysing mutation schemes for real-parameter genetic algorithms. Int J Artif Intell Soft Comput 4(1):1–28. https://doi.org/10.1504/IJAISC.2014.059280

    Google Scholar 

  26. Deb K (2005) A population-based algorithm-generator for real-parameter optimization. Soft Comput 9 (4):236–253. https://doi.org/10.1007/s00500-004-0377-4

    MATH  Google Scholar 

  27. Li K, Fialho A, Kwong S, Zhang Q (2014) Adaptive operator selection with bandits for a multiobjective evolutionary algorithm based on decomposition. IEEE Trans Evol Comput 18(1):114–130

    Google Scholar 

  28. Pal M, Saha S, Bandyopadhyay S (2018) DECOR: differential evolution using clustering based objective reduction for many-objective optimization. Inform Sci 423:200–218. https://doi.org/10.1016/j.ins.2017.09.051. http://www.sciencedirect.com/science/article/pii/S0020025517309696

    MathSciNet  Google Scholar 

  29. García S, Molina D, Lozano M, Herrera F (2009) A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: a case study on the CEC’2005 special session on real parameter optimization. J Heurist 15(6):617–644

    MATH  Google Scholar 

  30. Caamaño P., Bellas F, Becerra JA, Duro RJ (2008) Application domain study of evolutionary algorithms in optimization problems. In: Proceedings of the 10th annual conference on genetic and evolutionary computation, pp 495–502

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Authors and Affiliations

  1. Department of Computer Science and Technology, Indian Institute of Technology, Patna, India

    Raunak Sengupta & Sriparna Saha

  2. Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India

    Monalisa Pal & Sanghamitra Bandyopadhyay

Authors
  1. Raunak Sengupta
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  2. Monalisa Pal
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  3. Sriparna Saha
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  4. Sanghamitra Bandyopadhyay
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Correspondence to Monalisa Pal.

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This work is partially supported by the project (DST-INRIA/2015-02/BIDEE/0978) of Indo-French Centre for the Promotion of Advanced Research (CEFIPRA—IFCPAR), by J. C. Bose Fellowship (SB/SJ/JCB-033/2016) and “SERB Women Excellence Award 2018” (SB/WEA-08/2017) of Department of Science and Technology, Government of India.

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Sengupta, R., Pal, M., Saha, S. et al. Uniform distribution driven adaptive differential evolution. Appl Intell 50, 3638–3659 (2020). https://doi.org/10.1007/s10489-020-01707-2

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