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Harmony Search Algorithm pp 181–195Cite as

A New HMCR Parameter of Harmony Search for Better Exploration

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 382))

Abstract

As a meta-heuristic algorithm, Harmony Search (HS) algorithm is a population-based meta-heuristics approach that is superior in solving diversified large scale optimization problems. Several studies have pointed that Harmony Search (HS) is an efficient and flexible tool to resolve optimization problems in diversed areas of construction, engineering, robotics, telecommunication, health and energy. In this respect, the three main operators in HS, namely the Harmony Memory Consideration Rate (HMCR), Pitch Adjustment Rate (PAR) and Bandwidth (BW) play a vital role in balancing the local exploitation and the global exploration. These parameters influence the overall performance of HS algorithm, and therefore it is very crucial to fine turn them. However, when performing a local search, the harmony search algorithm can be easily trapped in the local optima. Therefore, there is a need to improve the fine tuning of the parameters. This research focuses on the HMCR parameter adjustment strategy using step function with combined Gaussian distribution function to enhance the global optimality of HS. The result of the study showed a better global optimum in comparison to the standard HS.

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References

  1. Russell, S., Norvig, P.: Artificial intelligence: A modern approach, vol. 25. Prentice-Hall, Egnlewood Cliffs (1995)

    MATH  Google Scholar 

  2. Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Ad-dison-Wesley Longman Publishing Co., Inc., Boston (1989)

    MATH  Google Scholar 

  3. Dorigo, M., Maniezzo, V., Colorni, A.: Ant system: optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 26(1), 29–41 (1996)

    Article  Google Scholar 

  4. Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proc. IEEE Intl. Conf. on Neural Networks. IEEE Service Center, Piscataway, pp. 1942–1948 (1995)

    Google Scholar 

  5. Walker, A., Hallam, J., Willshaw, D.: Bee-havior in a mobile robot: The construction of a self-organized cognitive map and its use in robot navigation within a complex, natural environment. In: IEEE International Conference on Neural Networks, pp. 1451–1456 (1993)

    Google Scholar 

  6. Geem, Z.W., Kim, J.H., Loganathan, G.V.: A new heuristic optimization algorithm: harmony search. Simulation 76(2), 60–68 (2001)

    Article  Google Scholar 

  7. Al-Betar, M.A., Doush, I.A., Khader, A.T., Awadallah, M.A.: Novel selection schemes for harmony search. Applied Mathematics and Computation 218(10), 6095–6117 (2012)

    Article  MATH  Google Scholar 

  8. Al-Betar, M.A., Khader, A.T., Zaman, M.: University course timetabling using a hybrid harmony search metaheuristic algorithm. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 42(5), 664–681 (2012)

    Article  Google Scholar 

  9. Mahdavi, M., Fesanghary, M., Damangir, E.: An improved harmony search algorithm for solving optimization problems. Applied mathematics and computation 188(2), 1567–1579 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  10. Sharma, K.D., Chatterjee, A., Rakshit, A.: Design of a hybrid stable adaptive fuzzy controller employing Lyapunov theory and harmony search algorithm. IEEE Transactions on Control Systems Technology 18(6), 1440–1447 (2010)

    Google Scholar 

  11. Ayvaz, M.T.: Application of harmony search algorithm to the solution of groundwater management models. Advances in Water Resources 32(6), 916–924 (2009)

    Article  Google Scholar 

  12. Ahmad, I., Mohammad, M.G., Salman, A.A., Hamdan, S.A.: Broadcast scheduling in packet radio networks using Harmony Search algorithm. Expert Systems with Applications 39(1), 1526–1535 (2012)

    Article  Google Scholar 

  13. Sivasubramani, S., Swarup, K.S.: Environmental/economic dispatch using multi-objective harmony search algorithm. Electric Power Systems Research 81(9), 1778–1785 (2011)

    Article  Google Scholar 

  14. Omran, M.G., Mahdavi, M.: Global-best harmony search. Applied Mathematics and Computation 198(2), 643–656 (2008)

    Article  MATH  MathSciNet  Google Scholar 

  15. Pan, Q.K., Suganthan, P.N., Tasgetiren, M.F., Liang, J.J.: A self-adaptive global best harmony search algorithm for continuous optimization problems. Applied Mathematics and Computation 216(3), 830–848 (2010)

    Article  MATH  MathSciNet  Google Scholar 

  16. Wang, C.M., Huang, Y.F.: Self-adaptive harmony search algorithm for optimization. Expert Systems with Applications 37(4), 2826–2837 (2010)

    Article  Google Scholar 

  17. Chang-ming, X., Lin, Y.: Research on adjustment strategy of PAR in harmony search algorithm. In: International Conference on Automatic Control and Artificial Intelligence (ACAI 2012), pp. 1705–1708 (2012)

    Google Scholar 

  18. Al-Betar, M.A., Khader, A.T., Geem, Z.W., Doush, I.A., Awadallah, M.A.: An analysis of selection methods in memory consideration for harmony search. Applied Mathematics and Computation 219(22), 10753–10767 (2013)

    Article  MATH  MathSciNet  Google Scholar 

  19. Kumar, V., Chhabra, J.K., Kumar, D.: Parameter adaptive harmony search algorithm for unimodal and multimodal optimization problems. Journal of Computational Science 5(2), 144–155 (2014)

    Article  MathSciNet  Google Scholar 

  20. Ayvaz, M.T.: Identification of groundwater parameter structure using harmony search algorithm. Studies in Computational Intelligence 191, 129 (2009)

    Article  Google Scholar 

  21. Using, B.U.S., Search, H., Shaffiei, Z.A., Abas, Z.A., Nizam, A.F., Rahman, A.: Optimization in Driver’S Scheduling For University, pp. 15–16 (2014)

    Google Scholar 

  22. Abas, Z.A., Binti Shaffiei, Z.A., Rahman, A.N.A., Shibghatullah, A.S.: Using Harmony Search For Optimising University Shuttle Bus Driver Scheduling For Better Operational Management

    Google Scholar 

  23. Caponetto, R., Fortuna, L., Fazzino, S., Xibilia, M.G.: Chaotic sequences to improve the performance of evolutionary algorithms. IEEE Transactions on Evolutionary Computation 7(3), 289–304 (2003)

    Article  Google Scholar 

  24. Krohling, R.: Gaussian swarm: a novel particle swarm optimization algorithm. In: 2004 IEEE Conference on Cybernetics and Intelligent Systems, vol. 1, pp. 372–376 (2004)

    Google Scholar 

  25. Khilwani, N., Prakash, A., Shankar, R., Tiwari, M.K.: Fast clonal algorithm. Engineering Applications of Artificial Intelligence 21(1), 106–128 (2008)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Faculty of Informatics and Computing, Universiti Sultan Zainal Abidin (Tembila Campus), 22200, Besut, Terengganu, Malaysia

    Nur Farraliza Mansor

  2. Optimization, Modelling, Analysis, Simulation and Scheduling (OptiMASS) Research Group, Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka (UTeM), Hang Tuah Jaya, 76100, Durian Tunggal, Melaka, Malaysia

    Zuraida Abal Abas, Ahmad Fadzli Nizam Abdul Rahman & Abdul Samad Shibghatullah

  3. Center for Languages and Human Development, Universiti Teknikal Malaysia Melaka (UTeM), Hang Tuah Jaya, 76100, Durian Tunggal, Melaka, Malaysia

    Safiah Sidek

Authors
  1. Nur Farraliza Mansor
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  2. Zuraida Abal Abas
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  3. Ahmad Fadzli Nizam Abdul Rahman
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  4. Abdul Samad Shibghatullah
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  5. Safiah Sidek
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Corresponding author

Correspondence to Nur Farraliza Mansor .

Editor information

Editors and Affiliations

  1. Civil, Environmental and Architectural Engineering, Korea University, Seoul, Korea (Republic of)

    Joong Hoon Kim

  2. Energy IT, Gachon University, Seongnam-Si, Korea (Republic of)

    Zong Woo Geem

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© 2016 Springer-Verlag Berlin Heidelberg

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Mansor, N.F., Abas, Z.A., Rahman, A.F.N.A., Shibghatullah, A.S., Sidek, S. (2016). A New HMCR Parameter of Harmony Search for Better Exploration. In: Kim, J., Geem, Z. (eds) Harmony Search Algorithm. Advances in Intelligent Systems and Computing, vol 382. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-47926-1_18

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  • DOI: https://doi.org/10.1007/978-3-662-47926-1_18

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-47925-4

  • Online ISBN: 978-3-662-47926-1

  • eBook Packages: EngineeringEngineering (R0)

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