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A Hybrid Multi-Objective Bat Algorithm for Solving Cloud Computing Resource Scheduling Problems

Author

Listed:
  • Jianguo Zheng

    (Glorious Sun School of Business and Management, Donghua University, Shanghai 200051, China)

  • Yilin Wang

    (Glorious Sun School of Business and Management, Donghua University, Shanghai 200051, China)

Abstract

To improve the service quality of cloud computing, and aiming at the characteristics of resource scheduling optimization problems, this paper proposes a hybrid multi-objective bat algorithm. To prevent the algorithm from falling into a local minimum, the bat population is classified. The back-propagation algorithm based on the mean square error and the conjugate gradient method is used to increase the loudness in the search direction and the pulse emission rate. In addition, the random walk based on lévy flight is also used to improve the optimal solution, thereby improving the algorithm’s global search capability. The simulation results prove that the multi-objective bat algorithm proposed in this paper is superior to the multi-objective ant colony optimization algorithm, genetic algorithm, particle swarm algorithm, and cuckoo search algorithm in terms of makespan, degree of imbalance, and throughput. The cost is also slightly better than the multi-objective ant colony optimization algorithm and the multi-objective genetic algorithm.

Suggested Citation

  • Jianguo Zheng & Yilin Wang, 2021. "A Hybrid Multi-Objective Bat Algorithm for Solving Cloud Computing Resource Scheduling Problems," Sustainability, MDPI, vol. 13(14), pages 1-25, July.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:14:p:7933-:d:595178
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    References listed on IDEAS

    as
    1. Muhammad Sardaraz & Muhammad Tahir, 2020. "A parallel multi-objective genetic algorithm for scheduling scientific workflows in cloud computing," International Journal of Distributed Sensor Networks, , vol. 16(8), pages 15501477209, August.
    2. Shafi’i Muhammad Abdulhamid & Muhammad Shafie Abd Latiff & Gaddafi Abdul-Salaam & Syed Hamid Hussain Madni, 2016. "Secure Scientific Applications Scheduling Technique for Cloud Computing Environment Using Global League Championship Algorithm," PLOS ONE, Public Library of Science, vol. 11(7), pages 1-18, July.
    3. Jie Luo & Ran Ren & Kangde Guo, 2020. "The deformation monitoring of foundation pit by back propagation neural network and genetic algorithm and its application in geotechnical engineering," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-14, July.
    4. Leila Hosseini & Shaojie Tang & Vijay Mookerjee & Chelliah Sriskandarajah, 2020. "A Switch in Time Saves the Dime: A Model to Reduce Rental Cost in Cloud Computing," Information Systems Research, INFORMS, vol. 31(3), pages 753-775, September.
    5. Mohammed Abdullahi & Md Asri Ngadi, 2016. "Hybrid Symbiotic Organisms Search Optimization Algorithm for Scheduling of Tasks on Cloud Computing Environment," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-29, June.
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    Cited by:

    1. Honghuan Chen & Keming Wang, 2023. "Fusing DCN and BBAV for Remote Sensing Image Object Detection," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), IGI Global, vol. 17(1), pages 1-16, January.

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