IDEAS home Printed from https://ideas.repec.org/a/eee/apmaco/v232y2014icp1166-1182.html
   My bibliography  Save this article

Hybridizing Harmony Search algorithm with different mutation operators for continuous problems

Author

Listed:
  • Hasan, Basima Hani F.
  • Abu Doush, Iyad
  • Al Maghayreh, Eslam
  • Alkhateeb, Faisal
  • Hamdan, Mohammad

Abstract

Harmony Search (HS) is a recent EA inspired by musical improvisation process to seek a pleasing harmony. Mutation is a vital component used in Evolutionary Algorithms (EA) where a value in the population is randomly selected to be altered to improve the evolution process. The original HS algorithm applies an operation similar to mutation during the random consideration operator. During random selection operator a value within the range of the decision variable is selected randomly to explore different areas in the search space. This paper aims at experimentally evaluating the performance of HS algorithm after replacing the random consideration operator in the original HS with five different mutation methods. The different variations of HS are experimented on standard benchmark functions in terms of final obtained solution and convergence speed. The results show that using polynomial mutation improves the performance of the HS algorithm for most of the used functions.

Suggested Citation

  • Hasan, Basima Hani F. & Abu Doush, Iyad & Al Maghayreh, Eslam & Alkhateeb, Faisal & Hamdan, Mohammad, 2014. "Hybridizing Harmony Search algorithm with different mutation operators for continuous problems," Applied Mathematics and Computation, Elsevier, vol. 232(C), pages 1166-1182.
    • Handle: RePEc:eee:apmaco:v:232:y:2014:i:c:p:1166-1182
    DOI: 10.1016/j.amc.2013.12.139
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S009630031301429X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.amc.2013.12.139?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Deb, Kalyanmoy & Tiwari, Santosh, 2008. "Omni-optimizer: A generic evolutionary algorithm for single and multi-objective optimization," European Journal of Operational Research, Elsevier, vol. 185(3), pages 1062-1087, March.
    2. Mohammed Al-Betar & Ahamad Khader, 2012. "A harmony search algorithm for university course timetabling," Annals of Operations Research, Springer, vol. 194(1), pages 3-31, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chen Zhang & Zhiwei Ni & Liping Ni & Na Tang, 2016. "Feature selection method based on multi-fractal dimension and harmony search algorithm and its application," International Journal of Systems Science, Taylor & Francis Journals, vol. 47(14), pages 3476-3486, October.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Vahid Baradaran & Amir Hossein Hosseinian, 2020. "A bi-objective model for redundancy allocation problem in designing server farms: mathematical formulation and solution approaches," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(5), pages 935-952, October.
    2. Liagkouras, Konstantinos & Metaxiotis, Konstantinos, 2021. "Improving multi-objective algorithms performance by emulating behaviors from the human social analogue in candidate solutions," European Journal of Operational Research, Elsevier, vol. 292(3), pages 1019-1036.
    3. Assif Assad & Kusum Deep, 2018. "Harmony search based memetic algorithms for solving sudoku," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 9(4), pages 741-754, August.
    4. Mohammed Al-Betar & Ahamad Khader & Iyad Doush, 2014. "Memetic techniques for examination timetabling," Annals of Operations Research, Springer, vol. 218(1), pages 23-50, July.
    5. K. Liagkouras & K. Metaxiotis, 2019. "Improving the performance of evolutionary algorithms: a new approach utilizing information from the evolutionary process and its application to the fuzzy portfolio optimization problem," Annals of Operations Research, Springer, vol. 272(1), pages 119-137, January.
    6. Dan Yan & Saskia E. Werners & He Qing Huang & Fulco Ludwig, 2016. "Identifying and Assessing Robust Water Allocation Plans for Deltas Under Climate Change," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(14), pages 5421-5435, November.
    7. Abha Sharma & Pushpendra Kumar & Kanojia Sindhuben Babulal & Ahmed J. Obaid & Harshita Patel, 2022. "Categorical Data Clustering Using Harmony Search Algorithm for Healthcare Datasets," International Journal of E-Health and Medical Communications (IJEHMC), IGI Global, vol. 13(4), pages 1-15, August.
    8. R. A. Oude Vrielink & E. A. Jansen & E. W. Hans & J. Hillegersberg, 2019. "Practices in timetabling in higher education institutions: a systematic review," Annals of Operations Research, Springer, vol. 275(1), pages 145-160, April.
    9. MinJun Kim & JuneSeok Hong & Wooju Kim, 2019. "An Efficient Representation Using Harmony Search for Solving the Virtual Machine Consolidation," Sustainability, MDPI, vol. 11(21), pages 1-20, October.
    10. Wei Sun & Yujun He & Hong Chang, 2015. "Forecasting Fossil Fuel Energy Consumption for Power Generation Using QHSA-Based LSSVM Model," Energies, MDPI, vol. 8(2), pages 1-21, January.
    11. Wang, Long & Wu, Jianghai & Wang, Tongguang & Han, Ran, 2020. "An optimization method based on random fork tree coding for the electrical networks of offshore wind farms," Renewable Energy, Elsevier, vol. 147(P1), pages 1340-1351.
    12. Wang, Long & Wang, Tongguang & Wu, Jianghai & Chen, Guoping, 2017. "Multi-objective differential evolution optimization based on uniform decomposition for wind turbine blade design," Energy, Elsevier, vol. 120(C), pages 346-361.
    13. Johnes, Jill, 2015. "Operational Research in education," European Journal of Operational Research, Elsevier, vol. 243(3), pages 683-696.
    14. K. Liagkouras & K. Metaxiotis & G. Tsihrintzis, 2022. "Incorporating environmental and social considerations into the portfolio optimization process," Annals of Operations Research, Springer, vol. 316(2), pages 1493-1518, September.
    15. F. Paola & M. Giugni & F. Pugliese & P. Romano, 2018. "Optimal Design of LIDs in Urban Stormwater Systems Using a Harmony-Search Decision Support System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(15), pages 4933-4951, December.
    16. Long Wang & Jianghai Wu & Zeling Tang & Tongguang Wang, 2019. "An Integration Optimization Method for Power Collection Systems of Offshore Wind Farms," Energies, MDPI, vol. 12(20), pages 1-16, October.
    17. Lahasan, Badr Mohammed & Venkat, Ibrahim & Al-Betar, Mohammed Azmi & Lutfi, Syaheerah Lebai & Wilde, Philippe De, 2016. "Recognizing faces prone to occlusions and common variations using optimal face subgraphs," Applied Mathematics and Computation, Elsevier, vol. 283(C), pages 316-332.
    18. K. Liagkouras & K. Metaxiotis, 2018. "A new efficiently encoded multiobjective algorithm for the solution of the cardinality constrained portfolio optimization problem," Annals of Operations Research, Springer, vol. 267(1), pages 281-319, August.
    19. Islam, Samiul & Amin, Saman Hassanzadeh & Wardley, Leslie J., 2021. "Machine learning and optimization models for supplier selection and order allocation planning," International Journal of Production Economics, Elsevier, vol. 242(C).
    20. Audet, Charles & Bigeon, Jean & Cartier, Dominique & Le Digabel, Sébastien & Salomon, Ludovic, 2021. "Performance indicators in multiobjective optimization," European Journal of Operational Research, Elsevier, vol. 292(2), pages 397-422.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:apmaco:v:232:y:2014:i:c:p:1166-1182. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/applied-mathematics-and-computation .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.