IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v537y2020ics0378437119315894.html
   My bibliography  Save this article

STSA: A sine Tree-Seed Algorithm for complex continuous optimization problems

Author

Listed:
  • Jiang, Jianhua
  • Xu, Meirong
  • Meng, Xianqiu
  • Li, Keqin

Abstract

Tree-Seed Algorithm (TSA) has good performance in solving various optimization problems. However, it is inevitable to suffer from slow exploitation when solving complex problems. This paper makes an intensive analysis of TSA. In order to keep the balance between exploration and exploitation, we propose an adaptive automatic adjustment mechanism. The number of seeds can be defined in the initialization process of the optimization algorithm. In order to further improve the convergence rate of TSA, we also modify the change model of seed numbers in the initialization process with randomly changing from more to less. With the improvement of two mechanisms, the main weakness of TSA has been overcome effectively. Based on the above two improvements, we propose a new algorithm-Sine Tree-Seed Algorithm (STSA). STSA achieves good results in solving high-dimensional complex optimization problems. The results obtained from 24 benchmark functions confirm the excellent performance of the proposed method.

Suggested Citation

  • Jiang, Jianhua & Xu, Meirong & Meng, Xianqiu & Li, Keqin, 2020. "STSA: A sine Tree-Seed Algorithm for complex continuous optimization problems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 537(C).
    • Handle: RePEc:eee:phsmap:v:537:y:2020:i:c:s0378437119315894
    DOI: 10.1016/j.physa.2019.122802
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437119315894
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2019.122802?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. Moradi, Mehdi & Parsa, Saeed, 2019. "An evolutionary method for community detection using a novel local search strategy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 457-475.
    2. Marouani, H. & Fouad, Y., 2019. "Particle swarm optimization performance for fitting of Lévy noise data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 708-714.
    3. Yang, Yong & Tu, Lilan & Li, Kuanyang & Guo, Tianjiao, 2019. "Optimized inter-structure for enhancing the synchronizability of interdependent networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 310-318.
    4. Nawaz, Muhammad & Enscore Jr, E Emory & Ham, Inyong, 1983. "A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem," Omega, Elsevier, vol. 11(1), pages 91-95.
    5. Yang, Benhao & Yang, Shunkun & Zhang, Jiaquan & Li, Daqing, 2018. "Optimizing random searches on three-dimensional lattices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 501(C), pages 120-125.
    6. Tang, Jianxin & Zhang, Ruisheng & Yao, Yabing & Yang, Fan & Zhao, Zhili & Hu, Rongjing & Yuan, Yongna, 2019. "Identification of top-k influential nodes based on enhanced discrete particle swarm optimization for influence maximization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 477-496.
    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. Rezk, Hegazy & Olabi, A.G. & Ferahtia, Seydali & Sayed, Enas Taha, 2022. "Accurate parameter estimation methodology applied to model proton exchange membrane fuel cell," Energy, Elsevier, vol. 255(C).
    2. Abdel-Basset, Mohamed & Mohamed, Reda & Abouhawwash, Mohamed, 2023. "On the facile and accurate determination of the highly accurate recent methods to optimize the parameters of different fuel cells: Simulations and analysis," Energy, Elsevier, vol. 272(C).
    3. Hegazy Rezk & A. G. Olabi & Tabbi Wilberforce & Enas Taha Sayed, 2023. "A Comprehensive Review and Application of Metaheuristics in Solving the Optimal Parameter Identification Problems," Sustainability, MDPI, vol. 15(7), pages 1-24, March.

    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. Jiang, Jianhua & Yang, Xi & Meng, Xianqiu & Li, Keqin, 2020. "Enhance chaotic gravitational search algorithm (CGSA) by balance adjustment mechanism and sine randomness function for continuous optimization problems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 537(C).
    2. Andrzej Kozik, 2017. "Handling precedence constraints in scheduling problems by the sequence pair representation," Journal of Combinatorial Optimization, Springer, vol. 33(2), pages 445-472, February.
    3. Hongyu He & Yanzhi Zhao & Xiaojun Ma & Zheng-Guo Lv & Ji-Bo Wang, 2023. "Branch-and-Bound and Heuristic Algorithms for Group Scheduling with Due-Date Assignment and Resource Allocation," Mathematics, MDPI, vol. 11(23), pages 1-14, November.
    4. Sündüz Dağ, 2013. "An Application On Flowshop Scheduling," Alphanumeric Journal, Bahadir Fatih Yildirim, vol. 1(1), pages 47-56, December.
    5. Chen, Chuen-Lung & Vempati, Venkateswara S. & Aljaber, Nasser, 1995. "An application of genetic algorithms for flow shop problems," European Journal of Operational Research, Elsevier, vol. 80(2), pages 389-396, January.
    6. Solimanpur, M. & Vrat, Prem & Shankar, Ravi, 2004. "A heuristic to minimize makespan of cell scheduling problem," International Journal of Production Economics, Elsevier, vol. 88(3), pages 231-241, April.
    7. Barry B. & Quim Castellà & Angel A. & Helena Ramalhinho Lourenco & Manuel Mateo, 2012. "ILS-ESP: An Efficient, Simple, and Parameter-Free Algorithm for Solving the Permutation Flow-Shop Problem," Working Papers 636, Barcelona School of Economics.
    8. Gupta, Jatinder N.D. & Koulamas, Christos & Kyparisis, George J., 2006. "Performance guarantees for flowshop heuristics to minimize makespan," European Journal of Operational Research, Elsevier, vol. 169(3), pages 865-872, March.
    9. Yu-Jun Zheng & Yi-Chen Du & Wei-Guo Sheng & Hai-Feng Ling, 2019. "Collaborative Human–UAV Search and Rescue for Missing Tourists in Nature Reserves," Interfaces, INFORMS, vol. 49(5), pages 371-383, September.
    10. Brammer, Janis & Lutz, Bernhard & Neumann, Dirk, 2022. "Permutation flow shop scheduling with multiple lines and demand plans using reinforcement learning," European Journal of Operational Research, Elsevier, vol. 299(1), pages 75-86.
    11. Quang Chieu Ta & Jean-Charles Billaut & Jean-Louis Bouquard, 2018. "Matheuristic algorithms for minimizing total tardiness in the m-machine flow-shop scheduling problem," Journal of Intelligent Manufacturing, Springer, vol. 29(3), pages 617-628, March.
    12. Tseng, Lin-Yu & Lin, Ya-Tai, 2009. "A hybrid genetic local search algorithm for the permutation flowshop scheduling problem," European Journal of Operational Research, Elsevier, vol. 198(1), pages 84-92, October.
    13. Gerardo Minella & Rubén Ruiz & Michele Ciavotta, 2008. "A Review and Evaluation of Multiobjective Algorithms for the Flowshop Scheduling Problem," INFORMS Journal on Computing, INFORMS, vol. 20(3), pages 451-471, August.
    14. Ramalhinho Lourenco, Helena, 1996. "Sevast'yanov's algorithm for the flow-shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 91(1), pages 176-189, May.
    15. Ghorbanzadeh, Masoumeh & Ranjbar, Mohammad, 2023. "Energy-aware production scheduling in the flow shop environment under sequence-dependent setup times, group scheduling and renewable energy constraints," European Journal of Operational Research, Elsevier, vol. 307(2), pages 519-537.
    16. Vineet Jain & Tilak Raj, 2018. "An adaptive neuro-fuzzy inference system for makespan estimation of flexible manufacturing system assembly shop: a case study," 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(6), pages 1302-1314, December.
    17. Allahverdi, Ali, 2003. "The two- and m-machine flowshop scheduling problems with bicriteria of makespan and mean flowtime," European Journal of Operational Research, Elsevier, vol. 147(2), pages 373-396, June.
    18. Yong Wang & Yuting Wang & Yuyan Han, 2023. "A Variant Iterated Greedy Algorithm Integrating Multiple Decoding Rules for Hybrid Blocking Flow Shop Scheduling Problem," Mathematics, MDPI, vol. 11(11), pages 1-25, May.
    19. B-J Joo & Y-D Kim, 2009. "A branch-and-bound algorithm for a two-machine flowshop scheduling problem with limited waiting time constraints," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(4), pages 572-582, April.
    20. Jean-Paul Watson & Laura Barbulescu & L. Darrell Whitley & Adele E. Howe, 2002. "Contrasting Structured and Random Permutation Flow-Shop Scheduling Problems: Search-Space Topology and Algorithm Performance," INFORMS Journal on Computing, INFORMS, vol. 14(2), pages 98-123, May.

    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:phsmap:v:537:y:2020:i:c:s0378437119315894. 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: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    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.