IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v8y2020i9p1403-d402088.html
   My bibliography  Save this article

Hybrid Annealing Krill Herd and Quantum-Behaved Particle Swarm Optimization

Author

Listed:
  • Cheng-Long Wei

    (Department of Computer Science and Technology, Ocean University of China, Qingdao 266100, China)

  • Gai-Ge Wang

    (Department of Computer Science and Technology, Ocean University of China, Qingdao 266100, China
    Institute of Algorithm and Big Data Analysis, Northeast Normal University, Changchun 130117, China
    School of Computer Science and Information Technology, Northeast Normal University, Changchun 130117, China
    Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China)

Abstract

The particle swarm optimization algorithm (PSO) is not good at dealing with discrete optimization problems, and for the krill herd algorithm (KH), the ability of local search is relatively poor. In this paper, we optimized PSO by quantum behavior and optimized KH by simulated annealing, so a new hybrid algorithm, named the annealing krill quantum particle swarm optimization (AKQPSO) algorithm, is proposed, and is based on the annealing krill herd algorithm (AKH) and quantum particle swarm optimization algorithm (QPSO). QPSO has better performance in exploitation and AKH has better performance in exploration, so AKQPSO proposed on this basis increases the diversity of population individuals, and shows better performance in both exploitation and exploration. In addition, the quantum behavior increased the diversity of the population, and the simulated annealing strategy made the algorithm avoid falling into the local optimal value, which made the algorithm obtain better performance. The test set used in this paper is a classic 100-Digit Challenge problem, which was proposed at 2019 IEEE Congress on Evolutionary Computation (CEC 2019), and AKQPSO has achieved better performance on benchmark problems.

Suggested Citation

  • Cheng-Long Wei & Gai-Ge Wang, 2020. "Hybrid Annealing Krill Herd and Quantum-Behaved Particle Swarm Optimization," Mathematics, MDPI, vol. 8(9), pages 1-23, August.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:9:p:1403-:d:402088
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/8/9/1403/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/8/9/1403/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Dulebenets, Maxim A., 2019. "A Delayed Start Parallel Evolutionary Algorithm for just-in-time truck scheduling at a cross-docking facility," International Journal of Production Economics, Elsevier, vol. 212(C), pages 236-258.
    2. Zhao, Xuejing & Wang, Chen & Su, Jinxia & Wang, Jianzhou, 2019. "Research and application based on the swarm intelligence algorithm and artificial intelligence for wind farm decision system," Renewable Energy, Elsevier, vol. 134(C), pages 681-697.
    3. Gaige Wang & Lihong Guo, 2013. "A Novel Hybrid Bat Algorithm with Harmony Search for Global Numerical Optimization," Journal of Applied Mathematics, Hindawi, vol. 2013, pages 1-21, February.
    4. Hong Duan & Wei Zhao & Gaige Wang & Xuehua Feng, 2012. "Test-Sheet Composition Using Analytic Hierarchy Process and Hybrid Metaheuristic Algorithm TS/BBO," Mathematical Problems in Engineering, Hindawi, vol. 2012, pages 1-22, November.
    5. Gai-Ge Wang & Suash Deb & Xinchao Zhao & Zhihua Cui, 2018. "A new monarch butterfly optimization with an improved crossover operator," Operational Research, Springer, vol. 18(3), pages 731-755, October.
    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. Thirunavukkarasu, M. & Sawle, Yashwant & Lala, Himadri, 2023. "A comprehensive review on optimization of hybrid renewable energy systems using various optimization techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 176(C).

    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 Li & Lihong Guo & Yan Li & Chang Liu, 2019. "Enhancing Elephant Herding Optimization with Novel Individual Updating Strategies for Large-Scale Optimization Problems," Mathematics, MDPI, vol. 7(5), pages 1-35, April.
    2. Alma Rodríguez & Avelina Alejo-Reyes & Erik Cuevas & Francisco Beltran-Carbajal & Julio C. Rosas-Caro, 2020. "An Evolutionary Algorithm-Based PWM Strategy for a Hybrid Power Converter," Mathematics, MDPI, vol. 8(8), pages 1-18, July.
    3. Juan Li & Dan-dan Xiao & Hong Lei & Ting Zhang & Tian Tian, 2020. "Using Cuckoo Search Algorithm with Q -Learning and Genetic Operation to Solve the Problem of Logistics Distribution Center Location," Mathematics, MDPI, vol. 8(2), pages 1-32, January.
    4. Mohammad Amin Amani & Mohammad Mahdi Nasiri, 2023. "A novel cross docking system for distributing the perishable products considering preemption: a machine learning approach," Journal of Combinatorial Optimization, Springer, vol. 45(5), pages 1-32, July.
    5. Gui Li & Gai-Ge Wang & Shan Wang, 2021. "Two-Population Coevolutionary Algorithm with Dynamic Learning Strategy for Many-Objective Optimization," Mathematics, MDPI, vol. 9(4), pages 1-34, February.
    6. Kailai Ni & Jianzhou Wang & Guangyu Tang & Danxiang Wei, 2019. "Research and Application of a Novel Hybrid Model Based on a Deep Neural Network for Electricity Load Forecasting: A Case Study in Australia," Energies, MDPI, vol. 12(13), pages 1-30, June.
    7. Bingtao Quan & Sujian Li & Kuo-Jui Wu, 2022. "Optimizing the Vehicle Scheduling Problem for Just-in-Time Delivery Considering Carbon Emissions and Atmospheric Particulate Matter," Sustainability, MDPI, vol. 14(10), pages 1-19, May.
    8. Md Tariqul Islam & M. J. Hossain, 2023. "Artificial Intelligence for Hosting Capacity Analysis: A Systematic Literature Review," Energies, MDPI, vol. 16(4), pages 1-33, February.
    9. Meng, Anbo & Zhu, Zibin & Deng, Weisi & Ou, Zuhong & Lin, Shan & Wang, Chenen & Xu, Xuancong & Wang, Xiaolin & Yin, Hao & Luo, Jianqiang, 2022. "A novel wind power prediction approach using multivariate variational mode decomposition and multi-objective crisscross optimization based deep extreme learning machine," Energy, Elsevier, vol. 260(C).
    10. Jianzhou Wang & Chunying Wu & Tong Niu, 2019. "A Novel System for Wind Speed Forecasting Based on Multi-Objective Optimization and Echo State Network," Sustainability, MDPI, vol. 11(2), pages 1-34, January.
    11. Alaa Tharwat & Aboul Ella Hassanien, 2019. "Quantum-Behaved Particle Swarm Optimization for Parameter Optimization of Support Vector Machine," Journal of Classification, Springer;The Classification Society, vol. 36(3), pages 576-598, October.
    12. Yunes Almansoub & Ming Zhong & Asif Raza & Muhammad Safdar & Abdelghani Dahou & Mohammed A. A. Al-qaness, 2022. "Exploring the Effects of Transportation Supply on Mixed Land-Use at the Parcel Level," Land, MDPI, vol. 11(6), pages 1-28, May.
    13. Ibrahim Mohamed Diaaeldin & Mahmoud A. Attia & Amr K. Khamees & Othman A. M. Omar & Ahmed O. Badr, 2023. "A Novel Multiobjective Formulation for Optimal Wind Speed Modeling via a Mixture Probability Density Function," Mathematics, MDPI, vol. 11(6), pages 1-19, March.
    14. Jiamin Wei & YangQuan Chen & Yongguang Yu & Yuquan Chen, 2019. "Optimal Randomness in Swarm-Based Search," Mathematics, MDPI, vol. 7(9), pages 1-19, September.
    15. Oluwatosin Theophilus & Maxim A. Dulebenets & Junayed Pasha & Olumide F. Abioye & Masoud Kavoosi, 2019. "Truck Scheduling at Cross-Docking Terminals: A Follow-Up State-Of-The-Art Review," Sustainability, MDPI, vol. 11(19), pages 1-23, September.
    16. Li, Lei & Yin, Xiao-Li & Jia, Xin-Chun & Sobhani, Behrooz, 2020. "Day ahead powerful probabilistic wind power forecast using combined intelligent structure and fuzzy clustering algorithm," Energy, Elsevier, vol. 192(C).
    17. Guedes, Kevin S. & de Andrade, Carla F. & Rocha, Paulo A.C. & Mangueira, Rivanilso dos S. & de Moura, Elineudo P., 2020. "Performance analysis of metaheuristic optimization algorithms in estimating the parameters of several wind speed distributions," Applied Energy, Elsevier, vol. 268(C).
    18. Pooja Tiwari & Vishnu Narayan Mishra & Raghav Prasad Parouha, 2024. "Modified differential evolution to solve systems of nonlinear equations," OPSEARCH, Springer;Operational Research Society of India, vol. 61(4), pages 1968-2001, December.
    19. Hubert Ruta & Tomasz Krakowski & Paweł Lonkwic, 2022. "Optimisation of the Magnetic Circuit of a Measuring Head for Diagnostics of Steel-Polyurethane Load-Carrying Belts Using Numerical Methods," Sustainability, MDPI, vol. 14(5), pages 1-19, February.
    20. Yupeng Zhou & Jinshu Li & Yang Liu & Shuai Lv & Yong Lai & Jianan Wang, 2020. "Improved Memetic Algorithm for Solving the Minimum Weight Vertex Independent Dominating Set," Mathematics, MDPI, vol. 8(7), pages 1-17, July.

    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:gam:jmathe:v:8:y:2020:i:9:p:1403-:d:402088. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.