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

Min Zhang; Gang Li

doi:10.3934/dcdss.2019097

Article Contents

2019, Volume 12, Issue 4&5: 1413-1426. Doi: 10.3934/dcdss.2019097 open access

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

Min Zhang^1, , and
Gang Li^2,

1.
College of Applied Science, Jiangxi University of Science and Technology, Ganzhou, China
2.
School of Road and Bridge Engineering, Xinjiang Vocational & Technical College of Communications, Urumchi, China

* Corresponding author: Min Zhang
* Corresponding author: Min Zhang

Received: May 31, 2017

Revised: October 31, 2017

Published: March 2019

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Abstract

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.

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Mathematics Subject Classification: 31C20.

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Figure 1. Optimization mechanism of multi-scale variation

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

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

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

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Figure 5. DTLZ2-the proposed method

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Figure 6. DTLZ2-PSO

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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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