Journal of the Korea Academia-Industrial cooperation Society (한국산학기술학회논문지)

The Korea Academia-Industrial cooperation Society (한국산학기술학회)
권호 표지
DOI QR코드

DOI QR Code

Improvement of Search Efficiency in Optimization Algorithm using Self-adaptive Harmony Search Algorithms

매개변수 자가적응 화음탐색 알고리즘의 성능 비교를 통한 최적해 탐색 효율 향상

  • Choi, Young Hwan (School of Civil, Environmental, and Architectural Engineering, Korea University) ;
  • Lee, Ho Min (Research Center for Disaster Prevention Science and Technology, Korea University) ;
  • Yoo, Do Guen (Department of Civil Engineering, University of Suwon) ;
  • Kim, Joong Hoo (School of Civil, Environmental, and Architectural Engineering, Korea University)
  • 최영환 (고려대학교 건축사회환경공학부) ;
  • 이호민 (고려대학교 방재과학기술연구소) ;
  • 유도근 (수원대학교 건설환경공학) ;
  • 김중훈 (고려대학교 건축사회환경공학부)
  • Received : 2017.12.15
  • Accepted : 2018.01.05
  • Published : 2018.01.31
Download PDF
⟨ Previous Next ⟩

Abstract

In various engineering fields, determining the appropriate parameter set is a cumbersome and difficult task when solving optimization problems. Despite the appropriate parameter setting through parameter sensitivity analysis, there are limits to evaluating whether the parameters are appropriate for all optimization problems. For this reason, kinds of a Self-adaptive Harmony searches have been developed to solve various engineering problems by the appropriate setting of algorithm's own parameters according to the problem. In this study, various types of Self-adaptive Harmony searches were investigated and the characteristics of optimization were categorized. Six algorithms with a differentiation of optimization process were applied and compared with not only the mathematical optimization problem, but also the engineering problem, which has been applied widely in the algorithm performance comparisons. The performance of each algorithm was compared, and the statistical performance indicators were used to evaluate the application results quantitatively.

다양한 공학분야의 최적화 문제를 해결하기 위해 적절한 매개변수를 설정하기란 번거로운 작업이며, 매개변수 민감도 분석을 통해 적절한 매개변수를 설정하더라도 설정된 매개변수가 모든 문제에 적절한지 판단하기에는 한계가 있다. 이러한 이유로 매개변수를 문제에 따라 적절하게 설정하는 매개변수 자동검보정 (Self-adaptive) 화음탐색 알고리즘이 개발되고 발전하고 있다. 본 연구에서는 지금까지 개발된 자가적응형 하모니서치를 조사하고 그의 특성을 해탐색, 설정 매개변수, 적용성 등으로 구분하였으며, 이 중 매개변수 설정의 번거로움을 없애고, 적절한 매개변수 설정을 통해 해의 성능 향상을 위해 개발 된 6 가지 자가적응형 화음탐색 알고리즘을 선택하여 비교 분석을 수행하였다. 최적화 결과의 객관적인 비교를 위해 대표적인 수학적, 공학적 최적화 문제를 모두 적용 하였고, 다양한 성능 지수 (Performance index)를 사용하여 각 알고리즘의 성능을 정량적으로 비교하였다. 이것은 향후 신규 최적화 알고리즘을 개발하거나 해 탐색의 성능을 향상시키는 연구에 도움이 될 것으로 기대된다.

Keywords

References

  1. Z. W. Geem, Y. H. Cho, "Optimal design of water distribution networks using parameter-setting-free harmony search for two major parameters", Journal of Water Resources Planning and Management vol. 137, no. 4, pp. 377-380, 2010. DOI: https://doi.org/10.1061/(ASCE)WR.1943-5452.0000130
  2. K. Deb, H. G. Beyer, "Self-adaptive genetic algorithms with simulated binary crossover", Evolutionary computation, vol. 9, no. 2, pp. 197-221, 2001. DOI: https://doi.org/10.1162/106365601750190406
  3. A. Ismail, A. P. Engelbrecht, "Self-Adaptive Particle Swarm Optimization", Seal, vol. 7673, 2012. DOI: https://doi.org/10.1007/978-3-642-34859-4_23
  4. M. G. Omran, A., Salman, A. P. Engelbrecht, "Self-adaptive differential evolution", International Conference on Computational and Information Science. Springer, Berlin, Heidelberg, 2005.
  5. Z. W. Geem, J. H. Kim, G. V. Loganathan, "A new heuristic optimization algorithm: harmony search", simulation, vol. 76, no. 2, pp. 60-68, 2001 https://doi.org/10.1177/003754970107600201
  6. J. H. Kim, Z. W. Geem, E. S. Kim, "Parameter estimation of the nonlinear Muskingum model using harmony search", JAWRA Journal of the American Water Resources Association, vol. 37, no. 5, pp. 1131-1138, 2001. DOI: https://doi.org/10.1111/j.1752-1688.2001.tb03627.x
  7. Glover, F. "Heuristics for integer programming using surrogate constraints." Decision Sciences 8.1 (1977): 156-166. DOI: https://doi.org/10.1111/j.1540-5915.1977.tb01074.x
  8. S. Kirkpatrick, C. D. Gelatt, M. P. Vecchi, "Optimization by simulated annealing", science vol. 220, no. 4598, pp. 671-680, 1983. https://doi.org/10.1126/science.220.4598.671
  9. M. Dorigo, "Optimization, learning and natural algorithms", Ph. D. Thesis, Politecnico di Milano, Italy, 1992.
  10. R. Eberhart, J. Kennedy, "A new optimizer using particle swarm theory", Micro Machine and Human Science, Proceedings of the Sixth International Symposium on IEEE, 1995. DOI: https://doi.org/10.1109/MHS.1995.494215
  11. M.Mahdavi, M. Fesanghary, E. Damangir, "An improved harmony search algorithm for solving optimization problems", Applied mathematics and computation, vol. 188, no. 2, pp. 1567-1579 2007. DOI: https://doi.org/10.1016/j.amc.2006.11.033
  12. M. G. Omran, M. Mahdavi, "Global-best harmony search", Applied mathematics and computation, vol. 198, no. 2, pp. 643-656, 2008. DOI: https://doi.org/10.1016/j.amc.2007.09.004
  13. Q. K. Pan, P. N. Suganthan, M. F. Tasgetiren, J. J. Liang, "A self-adaptive global best harmony search algorithm for continuous optimization problems", Applied Mathematics and Computation, vol. 216, no. 3, pp. 830-848, 2010. DOI: https://doi.org/10.1016/j.amc.2010.01.088
  14. C. M. Wang, Y. F. Huang, "Self-adaptive harmony search algorithm for optimization", Expert Systems with Applications, vol. 37, no. 4, pp. 2826-2837, 2010. DOI: https://doi.org/10.1016/j.eswa.2009.09.008
  15. S. O. Degertekin, "Improved harmony search algorithms for sizing optimization of truss structures", Computers and structures, vol. 92, pp. 229-241, 2012. DOI: https://doi.org/10.1016/j.compstruc.2011.10.022
  16. J. Chen, H. F. Man, Y. M. Wang, "Novel Self-adaptive Harmony Search Algorithm for continuous optimization problems", Control Conference (CCC), 2011 30th Chinese. IEEE, 2011.
  17. K. Luo, "A novel self-adaptive harmony search algorithm", Journal of Applied Mathematics, vol. 2013, 2013. DOI: http://dx.doi.org/10.1155/2013/653749
  18. Z. W. Geem, "Parameter estimation of the nonlinear Muskingum model using parameter-setting-free harmony search", Journal of Hydrologic Engineering, vol. 16, no. 8, pp. 684-688, 2010. DOI: https://doi.org/10.1061/(ASCE)HE.1943-5584.0000352
  19. S. Jiang, Y. Zhang, P. Wang, M. Zheng, "An almost-parameter-free harmony search algorithm for groundwater pollution source identification", Water Science and Technology, vol. 68, no. 11, pp. 2359-2366, 2013. DOI: https://doi.org/10.2166/wst.2013.499
  20. Z. W. Geem, K. B. Sim, "Parameter-setting-free harmony search algorithm", Applied Mathematics and Computation, vol. 217, no. 8, pp. 3881-3889, 2010. DOI: https://doi.org/10.1016/j.amc.2010.09.049
  21. Z. W. Geem, "Economic dispatch using parametersetting- free harmony search", Journal of Applied Mathematics, vol. 2013, 2013.
  22. S. Kulluk, L. Ozbakir, A. Baykasoglu, "Self-adaptive global best harmony search algorithm for training neural networks", Procedia Computer Science vol. 3, pp. 282-286, 2011. DOI: https://doi.org/10.1016/j.procs.2010.12.048
  23. A. Kattan, A. Rosni, "A dynamic self-adaptive harmony search algorithm for continuous optimization problems", Applied Mathematics and Computation, vol. 219, no. 16, pp. 8542-8567, 2013. DOI: https://doi.org/10.1016/j.amc.2013.02.074
  24. D. S. Rani, N. Subrahmanyam, M. Sydulu, "Self adaptive harmony search algorithm for optimal capacitor placement on radial distribution systems", Energy Efficient Technologies for Sustainability (ICEETS), 2013 International Conference on. IEEE, 2013. DOI: https://doi.org/10.1109/ICEETS.2013.6533580
  25. L. Wang, R. Yang, Y. Xu, Q. Niu, P. M. Pardalos, M. Fei, "An improved adaptive binary harmony search algorithm", Information Sciences, vol. 232, pp. 58-87, 2013. DOI: https://doi.org/10.1016/j.ins.2012.12.043
  26. B. Naik, J. Nayak, H. S. Behera, A. Abraham, "A self adaptive harmony search based functional link higher order ANN for non-linear data classification", Neurocomputing, vol. 179, pp. 69-87, 2016. DOI: https://doi.org/10.1016/j.neucom.2015.11.051
  27. A. Rajagopalan, V. Sengoden, R. Govindasamy, "Solving economic load dispatch problems using chaotic self‐adaptive differential harmony search algorithm", International Transactions on Electrical Energy Systems, vol. 25, no. 5, pp. 845-858, 2015. DOI: https://doi.org/10.1002/etep.1877
  28. M. Shivaie, M. T. Ameli, M. S. Sepasian, P. D. Weinsier, V. Vahidinasab, "A multistage framework for reliability-based distribution expansion planning considering distributed generations by a self-adaptive global-based harmony search algorithm", Reliability Engineering & System Safety, vol. 139, pp. 68-81, 2015. DOI: https://doi.org/10.1016/j.ress.2015.03.001
  29. M. Z. Vahid, M. O. Sadegh, "A new method to reduce losses in distribution networks using system reconfiguration with distributed generations using self-adaptive harmony search algorithm", Fuzzy and Intelligent Systems (CFIS), 2015 4th Iranian Joint Congress on. IEEE, 2015. DOI: https://doi.org/10.1109/CFIS.2015.7391655
  30. H. H. Yan, J. H. Duan, B. Zhang, Q. K. Pan, "Harmony search algorithm with self-adaptive dynamic parameters", Control and Decision Conference (CCDC), 2015 27th Chinese. IEEE, 2015. DOI: https://doi.org/10.1109/CCDC.2015.7162104
  31. F. Zhao, Y. Liu, C. Zhang, J. Wang, "A self-adaptive harmony PSO search algorithm and its performance analysis", Expert Systems with Applications, vol. 42, no. 21, pp. 7436-7455, 2015. DOI: https://doi.org/10.1016/j.eswa.2015.05.035
  32. V. Kumar, J. K. Chhabra, D. Kumar, "Parameter adaptive harmony search algorithm for unimodal and multimodal optimization problems", Journal of Computational Science, vol. 5, no. 2, pp. 144-155, 2014. DOI: https://doi.org/10.1016/j.jocs.2013.12.001
  33. X. Dai, X. Yuan, Z. Zhang, "A self-adaptive multi-objective harmony search algorithm based on harmony memory variance", Applied Soft Computing, vol. 35, pp. 541-557, 2015. DOI: https://doi.org/10.1016/j.asoc.2015.06.027
  34. P. Sabarinath, M. R. Thansekhar, R. Saravanan, "Multiobjective optimization method based on adaptive parameter harmony search algorithm", Journal of Applied Mathematics, vol. 2015, 2015.
  35. L. A. Rastrigin, "Systems of extremal control. Theoretical Foundations of Engineering Cybernetics Series", Nauka, Moscow, 1974.
  36. L. C. W. Dixon, "The global optimization problem: an introduction", Towards Global Optimiation vol. 2, pp. 1-15, 1978.
  37. M. Molga, C. Smutnicki, "Test functions for optimization needs", Test functions for optimization needs, 2005.
  38. D. G. Yoo, H. M. Lee, A. Sadollah, J. H. Kim, "Optimal pipe size design for looped irrigation water supply system using harmony search: Saemangeum project area", The Scientific World Journal, vol. 2015, 2015. DOI: http://dx.doi.org/10.1155/2015/651763