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Multi-objective optimization algorithm based on improved particle swarm in cloud computing environment

  • * Corresponding author: Min Zhang

    * Corresponding author: Min Zhang 
Abstract Full Text(HTML) Figure(6) / Table(1) Related Papers Cited by
  • In cloud computing environment, in order to optimize the deployment scheduling of resources, it is necessary to improve the accuracy of the optimal solution, guarantee the convergence ability of the algorithm, and improve the performance of cloud computing. In this paper, a multi-objective optimization algorithm based on improved particle swarm is proposed. A multi-objective optimization model is built. Improved multi-scale particle swarm is used to optimize the built multi-objective model. The combination of the global search capability and the local search capability of the algorithm is realized by using Gaussian variation operator with varied scales. The large scale Gaussian variation operator with concussion characteristics can complete fast global search for decision space, so that particles can quickly locate the surrounding area of the optimal solution, which enhances the ability to escape the local optimal solution of the algorithm and avoids the occurrence of precocious convergence. The small scale variation operator gradually reduces the area near the optimal solution. Experimental results show that the improved particle swarm optimization algorithm can effectively improve the precision of the optimal solution and ensure the convergence of the algorithm.

    Mathematics Subject Classification: 31C20.

    Citation:

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  • Figure 1.  Optimization mechanism of multi-scale variation

    Figure 2.  Improved particle swarm optimization algorithm for multi-objective optimization problem

    Figure 3.  Completion time for the case number of tasks is 20

    Figure 4.  Completion time for the case number of tasks is 200

    Figure 5.  DTLZ2-the proposed method

    Figure 6.  DTLZ2-PSO

    Table 1.  Experimental parameter setting

    Algorithm Number of particles Size of non-inferior solutions Number of iterations Probability of intersecting Probability of variation Size of the real solution set
    NSGAII 160 50 100 0.9 0.1 500
    CMOPSO 160 50 100 Nonlinear decline 500
    SPEA2 160 50 100 1 1/n 500
    CDMOPSO 160 50 100 0.5 500
    The proposed algorithm 160 50 100 Decrease with the increase of k 500
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