IDEAS home Printed from https://ideas.repec.org/a/spr/operea/v20y2020i2d10.1007_s12351-017-0346-1.html
   My bibliography  Save this article

An oracle penalty and modified augmented Lagrangian methods with firefly algorithm for constrained optimization problems

Author

Listed:
  • Umesh Balande

    (VNIT)

  • Deepti Shrimankar

    (VNIT)

Abstract

Almost all engineering optimization problems in the real world are constrained in nature. Swarm intelligence is a bio-inspired technique based on studying and observing fireflies, ants, birds and fish in nature. Firefly algorithm (FA) is the most prominent swarm based metaheuristic algorithm used for solving a global optimization problem. This paper presents two new constrained optimization algorithms: (1) firefly algorithm with extended oracle penalty method (FA-EOPM) and (2) modified augmented Lagrangian with firefly algorithm (MAL-FA). These proposed algorithms are applied for solving classic thirteen benchmark constraint problems as well as a few good engineering problem designs. The efficiency, effectiveness, and performance of MAL-FA and FA-EOPM algorithms are estimated on the basis of statistical analysis such as best optimal value, worst value, mean value, p value and standard deviation value against the existing methods. The experimental results show that the proposed MAL-FA algorithm offers better outcomes for most of the cases in terms of the number of function evaluations compared to various optimization algorithms.

Suggested Citation

  • Umesh Balande & Deepti Shrimankar, 2020. "An oracle penalty and modified augmented Lagrangian methods with firefly algorithm for constrained optimization problems," Operational Research, Springer, vol. 20(2), pages 985-1010, June.
    • Handle: RePEc:spr:operea:v:20:y:2020:i:2:d:10.1007_s12351-017-0346-1
    DOI: 10.1007/s12351-017-0346-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12351-017-0346-1
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s12351-017-0346-1?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. Kalyanmoy Deb & Soumil Srivastava, 2012. "A genetic algorithm based augmented Lagrangian method for constrained optimization," Computational Optimization and Applications, Springer, vol. 53(3), pages 869-902, December.
    2. Lina Zhang & Liqiang Liu & Xin-She Yang & Yuntao Dai, 2016. "A Novel Hybrid Firefly Algorithm for Global Optimization," PLOS ONE, Public Library of Science, vol. 11(9), pages 1-17, September.
    3. Garg, Harish, 2016. "A hybrid PSO-GA algorithm for constrained optimization problems," Applied Mathematics and Computation, Elsevier, vol. 274(C), pages 292-305.
    4. Asghar Mahdavi & Mohammad Shiri, 2015. "An augmented Lagrangian ant colony based method for constrained optimization," Computational Optimization and Applications, Springer, vol. 60(1), pages 263-276, January.
    5. David W. Coit & Alice E. Smith & David M. Tate, 1996. "Adaptive Penalty Methods for Genetic Optimization of Constrained Combinatorial Problems," INFORMS Journal on Computing, INFORMS, vol. 8(2), pages 173-182, May.
    6. Martin Schlüter & Matthias Gerdts, 2010. "The oracle penalty method," Journal of Global Optimization, Springer, vol. 47(2), pages 293-325, June.
    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. Jinzhong Zhang & Tan Zhang & Gang Zhang & Min Kong, 2023. "Parameter optimization of PID controller based on an enhanced whale optimization algorithm for AVR system," Operational Research, Springer, vol. 23(3), pages 1-26, September.

    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. Javaid Ali & Muhammad Saeed & Muhammad Farhan Tabassam & Shaukat Iqbal, 2019. "Controlled showering optimization algorithm: an intelligent tool for decision making in global optimization," Computational and Mathematical Organization Theory, Springer, vol. 25(2), pages 132-164, June.
    2. J. Gago-Vargas & I. Hartillo & J. Puerto & J. Ucha, 2015. "An improved test set approach to nonlinear integer problems with applications to engineering design," Computational Optimization and Applications, Springer, vol. 62(2), pages 565-588, November.
    3. Maen Z. Kreishan & Ahmed F. Zobaa, 2023. "Scenario-Based Uncertainty Modeling for Power Management in Islanded Microgrid Using the Mixed-Integer Distributed Ant Colony Optimization," Energies, MDPI, vol. 16(10), pages 1-30, May.
    4. Bin Xu & Ping-An Zhong & Xinyu Wan & Weiguo Zhang & Xuan Chen, 2012. "Dynamic Feasible Region Genetic Algorithm for Optimal Operation of a Multi-Reservoir System," Energies, MDPI, vol. 5(8), pages 1-17, August.
    5. Rashika Gupta & Manju Agarwal, 2006. "Penalty guided genetic search for redundancy optimization in multi-state series-parallel power system," Journal of Combinatorial Optimization, Springer, vol. 12(3), pages 257-277, November.
    6. Huimin Fu & Ming Shi & Miaomiao Feng, 2023. "Capacity optimization strategy for energy storage system to ensure power supply," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 18, pages 622-627.
    7. Manoj Dhadwal & Sung Jung & Chang Kim, 2014. "Advanced particle swarm assisted genetic algorithm for constrained optimization problems," Computational Optimization and Applications, Springer, vol. 58(3), pages 781-806, July.
    8. Dunker, Thomas & Radons, Gunter & Westkamper, Engelbert, 2005. "Combining evolutionary computation and dynamic programming for solving a dynamic facility layout problem," European Journal of Operational Research, Elsevier, vol. 165(1), pages 55-69, August.
    9. M Bachlaus & N Shukla & M. K. Tiwari & R Shankar, 2006. "Optimization of system reliability using chaos-embedded self-organizing hierarchical particle swarm optimization," Journal of Risk and Reliability, , vol. 220(2), pages 77-91, December.
    10. Ivona Brajević & Jelena Ignjatović, 2019. "An upgraded firefly algorithm with feasibility-based rules for constrained engineering optimization problems," Journal of Intelligent Manufacturing, Springer, vol. 30(6), pages 2545-2574, August.
    11. Chun-Yao Lee & Guang-Lin Zhuo, 2021. "A Hybrid Whale Optimization Algorithm for Global Optimization," Mathematics, MDPI, vol. 9(13), pages 1-19, June.
    12. Nikolaos Ploskas & Nikolaos V. Sahinidis, 2022. "Review and comparison of algorithms and software for mixed-integer derivative-free optimization," Journal of Global Optimization, Springer, vol. 82(3), pages 433-462, March.
    13. Mona A. S. Ali & Fathimathul Rajeena P. P. & Diaa Salama Abd Elminaam, 2022. "A Feature Selection Based on Improved Artificial Hummingbird Algorithm Using Random Opposition-Based Learning for Solving Waste Classification Problem," Mathematics, MDPI, vol. 10(15), pages 1-34, July.
    14. Nafees Ahamad & Afzal Sikander & Gagan Singh, 2022. "Order diminution and its application in controller design using salp swarm optimization technique," 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. 13(2), pages 933-943, April.
    15. Yuji Nakagawa & Ross J. W. James & César Rego & Chanaka Edirisinghe, 2014. "Entropy-Based Optimization of Nonlinear Separable Discrete Decision Models," Management Science, INFORMS, vol. 60(3), pages 695-707, March.
    16. Kutlu Onay, Funda, 2023. "A novel improved chef-based optimization algorithm with Gaussian random walk-based diffusion process for global optimization and engineering problems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 212(C), pages 195-223.
    17. Adane Abebaw Gessesse & Rajashree Mishra & Mitali Madhumita Acharya & Kedar Nath Das, 2020. "Genetic algorithm based fuzzy programming approach for multi-objective linear fractional stochastic transportation problem involving four-parameter Burr distribution," 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(1), pages 93-109, February.
    18. Yassin Belkourchia & Mohamed Zeriab Es-Sadek & Lahcen Azrar, 2023. "New Hybrid Perturbed Projected Gradient and Simulated Annealing Algorithms for Global Optimization," Journal of Optimization Theory and Applications, Springer, vol. 197(2), pages 438-475, May.
    19. Fazli Wahid & Rozaida Ghazali & Lokman Hakim Ismail & Ali M. Algarwi Aseere, 2023. "An Optimal Neural Network for Hourly and Daily Energy Consumption Prediction in Buildings," International Journal of Swarm Intelligence Research (IJSIR), IGI Global, vol. 14(1), pages 1-13, January.
    20. Touqeer Ahmed Jumani & Mohd Wazir Mustafa & Nawaf N. Hamadneh & Samer H. Atawneh & Madihah Md. Rasid & Nayyar Hussain Mirjat & Muhammad Akram Bhayo & Ilyas Khan, 2020. "Computational Intelligence-Based Optimization Methods for Power Quality and Dynamic Response Enhancement of ac Microgrids," Energies, MDPI, vol. 13(16), pages 1-22, August.

    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:spr:operea:v:20:y:2020:i:2:d:10.1007_s12351-017-0346-1. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.