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Advances in Nature and Biologically Inspired Computing pp 87–96Cite as

Aesthetic Differential Evolution Algorithm for Solving Computationally Expensive Optimization Problems

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 419))

Abstract

The applications of Differential Evolution (DE) and the attraction of researchers towards it, shows that it is a simple, powerful, efficient as well as reliable evolutionary algorithm to solve optimization problems. In this study an improved DE called aesthetic DE algorithm (ADEA) is introduced to solve Computationally Expensive Optimization (CEO) problems discussed in competition of congress of evolutionary computation (CEC) 2014. ADEA uses the concept of mirror images to produce new decorative positions. The mirror is placed near the most beautiful (global best) individual to accentuate its attractiveness (significance). Simulated statistical results demonstrate the efficiency and ability of the proposal to obtain good results.

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References

  1. Storn, R., Price, K.: Different Evolution—A Simple and Efficient Heuristic for global Optimization over Continuous spaces, pp. 341–359, Dec 1997

    Google Scholar 

  2. Storn, R.., Price, K.: Differential evolution-a simple and efficient adaptive scheme for global optimization over continuous spaces, TR-95-012 (1995). http.icsi.berkeley.edu/~storn/litera.html

  3. Lampinen, J., Zelinka, I.: On stagnation of the differential evolution algorithm. In: Oˇsmera, P. (ed.) Proceedings of the 6th International Mendel Conference on Soft Computing, pp. 76–83 (2002)

    Google Scholar 

  4. Gämperle, R., Müller, S., Koumoutsakos, P.: A parameter study for differential evolution. In: Grmela, A., Mastorakis, N.E. (eds.) Advances in Intelligent Systems, Fuzzy Systems, Evolutionary Computation, pp. 293–298. WSEAS Press, Interlaken, Switzerland (2002)

    Google Scholar 

  5. Liu, Q.C.a.Q.Z.B., Liang, J.J., Suganthan, P.N., Qu, B.Y.: Problem Definitions and Evaluation Criteria for Computationally Expensive Single Objective Numerical Optimization, Computational Intelligence Laboratory and Nanyang Technological University, Zhengzhou and Singapore, Technical Report 2013

    Google Scholar 

  6. Arnob, G., Swagatam, D., Aritra, C., Ritwik, G.: An improved differential evolution algorithm with fitness-based adaptation of the control parameters. Inf. Sci. 181(18), 3749–3765 (2011)

    Article  Google Scholar 

  7. Dexuan, Z., Haikuan, L., Liqun, G., Steven, L.: An improved differential evolution algorithm for the task assignment problem. Eng. Appl. Artif. Intell. 24(4), 616–624 (2011)

    Article  Google Scholar 

  8. Ling, W., Xiping, F., Yunfei, M., Ilyas, M.M., Minrui, F.: A novel modified binary differential evolution algorithm and its applications. Neurocomputing 98, 55–75 (2012)

    Google Scholar 

  9. Mohamed, A.W., Sabry, H.Z.: Constrained optimization based on modified differential evolution algorithm. Inf. Sci. 194(1), 171–208 (2012)

    Article  Google Scholar 

  10. Dexuan, Z., Jianhua, W., Liqun. G., Steven, L.: A modified differential evolution algorithm for unconstrained optimization problems. Neurocomputing 120, 469–481 (2013)

    Google Scholar 

  11. Deng, W., Xinhua, Y., Li, Z., Meng, W., Yaqing, L., Yuanyuan, Li: An improved self-adaptive differential evolution algorithm and its application. Chemometr. Intell. Lab. Syst. 128(15), 66–76 (2013)

    Article  Google Scholar 

  12. Musrrat, A., Wook, A.C., Patrick, S.: Differential evolution algorithm for the selection of optimal scaling factors in image watermarking. Eng. Appl. Artif. Intell. 31, 15–26 (2014)

    Google Scholar 

  13. dos Santos Coelho L., Ayala H.V.H., Mariani V.C.: A self-adaptive chaotic differential evolution algorithm using gamma distribution for unconstrained global optimization. Appl. Math. Comput. 234, 452–459 (2014)

    Google Scholar 

  14. Mohamed Ali Wagdy: An improved differential evolution algorithm with triangular mutation for global numerical optimization. Comput. Ind. Eng. 85, 359–375 (2015)

    Article  Google Scholar 

  15. Wan-li, X., Ning, Z., Shou-feng, M., Xue-lei, M., Mei-qing, A.: A dynamic shuffled differential evolution algorithm for data clustering. Neurocomputing 158(22), 144–154 (2015)

    Google Scholar 

  16. Rammohan, M., Minho, L.: An evolving surrogate model-based differential evolution algorithm. Appl. Soft Comput. 34, 770–787 (2015)

    Article  Google Scholar 

  17. Mashwani Wali Khan, Enhanced versions of differential evolution: state-of-the-art survey. Int. J. Comput. Sci. Math. 5(2), 107–126

    Google Scholar 

  18. Gandomi, A.H.: Interior search algorithm (ISA): a novel approach for global optimization. ISA Trans. Elsevier 53(4), 1168–1183 (2014)

    Article  Google Scholar 

  19. Gandomi, A.H., Yang, X.S.: Evolutionary boundary constraint handling scheme. Neural Comput. Appl. 21(6), 1449–1462 (2012)

    Article  Google Scholar 

  20. Yu, C., Kelley, L., Zheng, S., Tan, Y.: Fireworks algorithm with differential mutation for solving the CEC 2014 competition problems, In: 2014 IEEE Congress on Evolutionary Computation (CEC), pp. 3238–3245. Beijing, China, 6–11 July 2014

    Google Scholar 

  21. Elsayed, S.M., Ray, T., Sarker, R.A.: A surrogate-assisted differential evolution algorithm with dynamic parameters selection for solving expensive optimization problems. In: 2014 IEEE Congress on Evolutionary Computation (CEC), pp. 1062–1068. Beijing, China, 6–11 July 2014

    Google Scholar 

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Author information

Authors and Affiliations

  1. Govt. College of Engineering and Technology, Bikaner, India

    Ajeet Singh Poonia

  2. Amity University Rajasthan, Noida, India

    Tarun Kumar Sharma, Shweta Sharma & Jitendra Rajpurohit

Authors
  1. Ajeet Singh Poonia
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  2. Tarun Kumar Sharma
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  3. Shweta Sharma
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  4. Jitendra Rajpurohit
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Corresponding author

Correspondence to Tarun Kumar Sharma .

Editor information

Editors and Affiliations

  1. School of Mathematics, Statistics and CS, University of KwaZulu-Natal, Pietermaritzburg, South Africa

    Nelishia Pillay

  2. Sch of IT, Dept of CS, University of Pretoria, Pretoria, South Africa

    Andries P. Engelbrecht

  3. Sci N/w for Innova and Research Exc, Machine Intelligence Research Labs, Auburn, Washington, USA

    Ajith Abraham

  4. Department of Computing Sciences,, Nelson Mandela Metropolitan University, Port Elizabeth, South Africa

    Mathys C. du Plessis

  5. Faculty of Electrical Engineering and, Technical University of Ostrava, Ostrava-Poruba, Czech Republic

    Václav Snášel

  6. Fakulti Teknologi Maklumat dan Komunikas, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Malaysia

    Azah Kamilah Muda

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© 2016 Springer International Publishing Switzerland

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Poonia, A.S., Sharma, T.K., Sharma, S., Rajpurohit, J. (2016). Aesthetic Differential Evolution Algorithm for Solving Computationally Expensive Optimization Problems. In: Pillay, N., Engelbrecht, A., Abraham, A., du Plessis, M., Snášel, V., Muda, A. (eds) Advances in Nature and Biologically Inspired Computing. Advances in Intelligent Systems and Computing, vol 419. Springer, Cham. https://doi.org/10.1007/978-3-319-27400-3_8

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  • DOI: https://doi.org/10.1007/978-3-319-27400-3_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27399-0

  • Online ISBN: 978-3-319-27400-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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