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A Self-Adaptive Differential Evolution Using a New Adaption Based Operator for Software Cost Estimation

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Abstract

Today, predicting software parameters accurately during the initial software development stage is one of the biggest challenges facing most companies. In this article, it was discussed how meta-heuristic algorithms are used to solve multiple optimization problems that arise in mathematical and software models. The proposed method for solving optimization problems employs new adaptive mutation operators by incorporating a new syndrome adaptive mutation operator, which provides more diversity among candidate solutions. Further, by comparing the proposed mutation operator method with standard meta-heuristic algorithms, these were able to select better mutation results for 24 benchmark functions. Furthermore, the proposed method is useful for solving software engineering issues, including estimating software costs, which accurately predicts software parameters by optimizing the effort and errors for the constructive cost model. In comparison with other standard optimization algorithms, the proposed algorithm has a better ability to predict costs.

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References

  1. R. Storn, Differrential evolution-a simple and efficient adaptive scheme for global optimization over continuous spaces. Int. Comput. Sci. Inst. 11, 558 (1995)

    Google Scholar 

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

    Article  MATH  Google Scholar 

  3. B. Boehm, C. Abts, S. Chulani, Software development cost estimation approaches–a survey. Ann. Softw. Eng. 10(1), 177–205 (2000)

    Article  MATH  Google Scholar 

  4. S.J. Huang, N.H. Chiu, Applying fuzzy neural network to estimate software development effort. Appl. Intell. 30(2), 73–83 (2009)

    Article  Google Scholar 

  5. S.P. Singh, A. Kumar, Software cost estimation using homeostasis mutation based differential evolution, in 2017 11th International Conference on Intelligent Systems and Control (ISCO). (IEEE, 2017), pp. 173–181

  6. S. Aljahdali, A.F. Sheta, Software effort estimation by tuning coocmo model parameters using differential evolution, in ACS/IEEE International Conference on Computer Systems and Applications-AICCSA 2010. (IEEE, 2010), pp. 1–6

  7. R. Beed, A. Roy, S. Sarkar, D. Bhattacharya, A hybrid multi-objective tour route optimization algorithm based on particle swarm optimization and artificial bee colony optimization. Comput. Intell. 36(3), 884–909 (2020)

    Article  Google Scholar 

  8. T.R. Benala, R. Mall, Dabe: differential evolution in analogy-based software development effort estimation. Swarm Evol. Comput. 38, 158–172 (2018)

    Article  Google Scholar 

  9. A. Puspaningrum, R. Sarno, A hybrid cuckoo optimization and harmony search algorithm for software cost estimation. Proc. Comput. Sci. 124, 461–469 (2017)

    Article  Google Scholar 

  10. S.P. Singh, V.P. Singh, A.K. Mehta, Differential evolution using homeostasis adaption based mutation operator and its application for software cost estimation. J. King Saud Univ.-Comput. Inform. Sci. 33(6), 740–752 (2021)

    Google Scholar 

  11. Z.M. Gu, G.G. Wang, Improving NSGA-iii algorithms with information feedback models for large-scale many-objective optimization. Fut. Gener. Comput. Syst. 107, 49–69 (2020)

    Article  Google Scholar 

  12. M.S. Khan, F. Jabeen, S. Ghouzali, Z. Rehman, S. Naz, W. Abdul, Metaheuristic algorithms in optimizing deep neural network model for software effort estimation. IEEE Access 9, 60309–60327 (2021)

    Article  Google Scholar 

  13. X.S. Yang, Multiobjective firefly algorithm for continuous optimization. Eng. Comput. 29(2), 175–184 (2013)

    Article  Google Scholar 

  14. E. Zitzler, K. Deb, L. Thiele, Comparison of multiobjective evolutionary algorithms: empirical results. Evol. Comput. 8(2), 173–195 (2000)

    Article  Google Scholar 

  15. L.L. Minku, X. Yao, Software effort estimation as a multiobjective learning problem. ACM Trans. Softw. Eng. Methodol. 22(4), 1–32 (2013)

    Article  Google Scholar 

  16. M. Ali, P. Siarry, M. Pant, An efficient differential evolution based algorithm for solving multi-objective optimization problems. Eur. J. Oper. Res. 217(2), 404–416 (2012)

    MATH  Google Scholar 

  17. A. Got, A. Moussaoui, D. Zouache, A guided population archive whale optimization algorithm for solving multiobjective optimization problems. Expert Syst. Appl. 141, 112972 (2020)

    Article  Google Scholar 

  18. N. Noman, H. Iba, Accelerating differential evolution using an adaptive local search. IEEE Trans. Evol. Comput. 12(1), 107–125 (2008)

    Article  Google Scholar 

  19. M.G. Omran, A. Salman, A.P. Engelbrecht, Self-adaptive differential evolution, in International conference on computational and information science. (Springer, 2005), pp. 192–199

  20. J.C. Nwaiwu, S.A. Oluwadare, Analytic study of fuzzy-based model for software cost estimation, in OcRI (2016), pp. 22–30

  21. S. Ezghari, A. Zahi, Uncertainty management in software effort estimation using a consistent fuzzy analogy-based method. Appl. Soft Comput. 67, 540–557 (2018)

    Article  Google Scholar 

  22. S. Arora, N. Mishra, Software cost estimation using artificial neural network, in Soft Computing: Theories and Applications. (Springer, 2018), pp. 51–58

  23. A.F. Sheta, S. Aljahdali, Software effort estimation inspired by cocomo and fp models: a fuzzy logic approach. Int. J. Adv. Comput. Sci. Appl. 4(11), 5008 (2013)

    Google Scholar 

  24. Syndrome factor:. URL https://en:wikipedia:org/wiki/Syndrome

  25. D. Corne, M. Dorigo, F. Glover, D. Dasgupta, P. Moscato, R. Poli, K.V. Price, New ideas in optimization (McGraw-Hill Ltd., London, 1999)

    Google Scholar 

  26. G.S. Rao, C.V.P. Krishna, K.R. Rao, Multi objective particle swarm optimization for software cost estimation, in ICT and Critical Infrastructure: Proceedings of the 48th Annual Convention of Computer Society of India-Vol I. (Springer, 2014), pp. 125–132

  27. S.P. Singh, A. Kumar, Multiobjective differential evolution using homeostasis based mutation for application in software cost estimation. Appl. Intell. 48(3), 628–650 (2018)

    Article  Google Scholar 

  28. P. Singal, A.C. Kumari, P. Sharma, Estimation of software development effort: a differential evolution approach. Proc. Comput. Sci. 167, 2643–2652 (2020)

    Article  Google Scholar 

  29. V. Resmi, S. Vijayalakshmi, Kernel fuzzy clustering with output layer self-connection recurrent neural networks for software cost estimation. J. Circuits Syst. Comput. 29(06), 2050091 (2020)

    Article  Google Scholar 

  30. URL http://coco.gforge.inria.fr/

  31. N. Hansen, A. Auger, S. Finck, R. Ros, Real-parameter black-box optimization benchmarking 2010: Experimental setup. Ph.D. thesis, INRIA (2010)

  32. N. Hansen, S. Finck, R. Ros, A. Auger, Real-parameter black-box optimization benchmarking 2009: Noiseless functions definitions. Ph.D. thesis, INRIA (2009)

  33. B. Chen, Y. Lin, W. Zeng, D. Zhang, Y.W. Si, Modified differential evolution algorithm using a new diversity maintenance strategy for multi-objective optimization problems. Appl. Intell. 43(1), 49–73 (2015)

    Article  Google Scholar 

  34. Z.Z. Liu, Y. Wang, S. Yang, Z. Cai, Differential evolution with a two-stage optimization mechanism for numerical optimization, in 2016 IEEE congress on evolutionary computation (CEC). (IEEE, 2016), pp. 3170–3177

  35. M. Ali, M. Pant, A. Abraham, A modified differential evolution algorithm and its application to engineering problems, in 2009 International Conference of Soft Computing and Pattern Recognition. (IEEE, 2009), pp. 196–201

  36. S.P. Singh, D.K. Singh, Differential evolution algorithm using enhance-based adaption mutant vector, in Advances in Data and Information Sciences. (Springer, 2020), pp. 227–235

  37. X. Li, L. Wang, Q. Jiang, N. Li, Differential evolution algorithm with multi-population cooperation and multi-strategy integration. Neurocomputing 421, 285–302 (2021)

    Article  Google Scholar 

  38. Nasa data set: Project 93:. URL http://promise:site:uottawa:ca/SERepository/datasets/cocomonasa2.arff

  39. C. Hari, P. Reddy, A fine parameter tuning for cocomo 81 software effort estimation using particle swarm optimization. J. Softw. Eng. 5(1), 38–48 (2011)

    Article  Google Scholar 

  40. S.P. Singh, Cost estimation model using enhance-based differential evolution algorithm. Iran J. Comput. Sci. 3(2), 115–126 (2020)

    Article  Google Scholar 

  41. Y. Li, Y. He, X. Liu, X. Guo, Z. Li, A novel discrete whale optimization algorithm for solving knapsack problems. Appl. Intell. 50(10), 3350–3366 (2020)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Compter Science and Engineering, National Institute of Technology, Jamshedpur, India

    Sunil Kumar Gouda & Ashok Kumar Mehta

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  1. Sunil Kumar Gouda
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  2. Ashok Kumar Mehta
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Gouda, S.K., Mehta, A.K. A Self-Adaptive Differential Evolution Using a New Adaption Based Operator for Software Cost Estimation. J. Inst. Eng. India Ser. B 104, 23–42 (2023). https://doi.org/10.1007/s40031-022-00801-y

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