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

Jianguo Zheng and Yilin Wang
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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

Sustainability, 2021, vol. 13, issue 14, 1-25

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.

Keywords: multi-objective; bat algorithm; resource scheduling problem; cloud computing; metaheuristic algorithms (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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