August & September  2019, 12(4&5): 1413-1426. doi: 10.3934/dcdss.2019097

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

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

Received  June 2017 Revised  November 2017 Published  November 2018

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.

Citation: Min Zhang, Gang Li. Multi-objective optimization algorithm based on improved particle swarm in cloud computing environment. Discrete & Continuous Dynamical Systems - S, 2019, 12 (4&5) : 1413-1426. doi: 10.3934/dcdss.2019097
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X. HeH. Guan and J. Qin, A hybrid wavelet neural network model with mutual information and particle swarm optimization for forecasting monthly rainfall, Journal of Hydrology, 17 (2015), 88-100.   Google Scholar

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W. Hu and G. G. Yen, Adaptive multiobjective particle swarm optimization based on parallel cell coordinate system, IEEE Transactions on Evolutionary Computation, 19 (2015), 1-18.   Google Scholar

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G. P. Karatzas and Z. Dokou, Optimal management of saltwater intrusion in the coastal aquifer of malia, crete (greece), using particle swarm optimization, Hydrogeology Journal, 23 (2015), 1181-1194.   Google Scholar

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C. Lee, Assigning the appropriate works for review on networked peer assessment, Eurasia Journal of Mathematics Science and Technology Education, 3 , 3283-3300. Google Scholar

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B. M. R. and M. Z., Analysis of stability, local convergence, and transformation sensitivity of a variant of the particle swarm optimization algorithm, in IEEE Transactions on Evolutionary Computation, 370-385. Google Scholar

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L. MaoW. GuiwuF. AlsaadiT. Hayat and A. Alsaedi, Hesitant pythagorean fuzzy hamacher aggregation operators and their application to multiple attribute decision making, Iran. J. Fuzzy Syst., 13 (2016), 1-16, 147.   Google Scholar

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S. L. Marie-Sainte, A survey of particle swarm optimization techniques for solving university examination timetabling problem, Artificial Intelligence Review, 44 (2015), 537-546.   Google Scholar

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M. Pelto, Maximum difference about the size of optimal identifying codes in graphs differing by one vertex, Discrete Mathematics & Theoretical Computer Science, 17 (2015), 339-356.   Google Scholar

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C. S., M. J. and B.-R. A., Measuring the Curse of Dimensionality and Its Effects on Particle Swarm Optimization and Differential Evolution, 3, Applied Intelligence, 2015. Google Scholar

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J. SunX. WuV. PaladeW. Fang and Y. Shi, Random drift particle swarm optimization algorithm: convergence analysis and parameter selection, Machine Learning, 101 (2015), 345-376.  doi: 10.1007/s10994-015-5522-z.  Google Scholar

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X. WangJ. Wu and L. Liu, The development of modern service industry in china and its influence factors, Journal of Interdisciplinary Mathematics,, 20 (2008), 161-171.   Google Scholar

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L. Y., Z. Z.H., L. S. and et al, Competitive and cooperative particle swarm optimization with information sharing mechanism for global optimization problems, European Journal of Operational Research, 3 (2015), 370-382. Google Scholar

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H. Q. Yan, Network intrusion small signal detection model based on optimization particle swarm algorithm, Bulletin of Science & Technology, 12 (2015), 193-195.   Google Scholar

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R. Yan, M. A. Xiao-Juan, T. Z. Lian and L. Wang, Research on optimal joint problem of routing and loading in military airlift, Journal of China Academy of Electronics & Information Technology, 7-13. Google Scholar

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B. YaoB. YuP. HuJ. Gao and M. Zhang, An improved particle swarm optimization for carton heterogeneous vehicle routing problem with a collection depot, Annals of Operations Research, 242 (2016), 303-320.  doi: 10.1007/s10479-015-1792-x.  Google Scholar

show all references

References:
[1]

Z. Beheshti, S. M. Shamsuddin and S. Hasan, Memetic binary particle swarm optimization for discrete optimization problems, Information Sciences, 58-84. Google Scholar

[2]

M. C. and Q. L., Multiobjective optimization of switched reluctance motors based on design of experiments and particle swarm optimization, Eurasia Journal of Mathematics Science &Technology Education, 3 (2017), 1144-1153. Google Scholar

[3]

G. H. ChenY. Zhao and B. Su, Raw material inventory optimization for mto enterprises under price fluctuations, Journal of Discrete Mathematical Sciences & Cryptography, 20 (2017), 255-270.   Google Scholar

[4]

H. L. Chen, B. Yang, S. J. Wang, G. Wang, D. Y. Liu, H. Z. Li and W. B. Liu, Dynamic multiobjective particle swarm optimization based on projection mapping, Computer Simulation, 233-238. Google Scholar

[5]

R. Cheng and Y. Jin, A social learning particle swarm optimization algorithm for scalable optimization, Information Sciences, 291 (2015), 43-60.  doi: 10.1016/j.ins.2014.08.039.  Google Scholar

[6]

J. M. Feng and S. Y. Liu, Particle swarm optimization algorithm based on inertia weight exponentially decreasing for solving absolute value equations, Journal of Jilin University (Science Edition), 54 (2016), 1265-1269.   Google Scholar

[7]

M. HajihassaniD. J. ArmaghaniM. MonjeziE. T. Mohamad and A. Marto, Blast-induced air and ground vibration prediction: A particle swarm optimization-based artificial neural network approach, Environmental Earth Sciences, 74 (2015), 2799-2817.   Google Scholar

[8]

X. HeH. Guan and J. Qin, A hybrid wavelet neural network model with mutual information and particle swarm optimization for forecasting monthly rainfall, Journal of Hydrology, 17 (2015), 88-100.   Google Scholar

[9]

W. Hu and G. G. Yen, Adaptive multiobjective particle swarm optimization based on parallel cell coordinate system, IEEE Transactions on Evolutionary Computation, 19 (2015), 1-18.   Google Scholar

[10]

G. P. Karatzas and Z. Dokou, Optimal management of saltwater intrusion in the coastal aquifer of malia, crete (greece), using particle swarm optimization, Hydrogeology Journal, 23 (2015), 1181-1194.   Google Scholar

[11]

C. Lee, Assigning the appropriate works for review on networked peer assessment, Eurasia Journal of Mathematics Science and Technology Education, 3 , 3283-3300. Google Scholar

[12]

B. M. R. and M. Z., Analysis of stability, local convergence, and transformation sensitivity of a variant of the particle swarm optimization algorithm, in IEEE Transactions on Evolutionary Computation, 370-385. Google Scholar

[13]

L. MaoW. GuiwuF. AlsaadiT. Hayat and A. Alsaedi, Hesitant pythagorean fuzzy hamacher aggregation operators and their application to multiple attribute decision making, Iran. J. Fuzzy Syst., 13 (2016), 1-16, 147.   Google Scholar

[14]

S. L. Marie-Sainte, A survey of particle swarm optimization techniques for solving university examination timetabling problem, Artificial Intelligence Review, 44 (2015), 537-546.   Google Scholar

[15]

M. Pelto, Maximum difference about the size of optimal identifying codes in graphs differing by one vertex, Discrete Mathematics & Theoretical Computer Science, 17 (2015), 339-356.   Google Scholar

[16]

C. S., M. J. and B.-R. A., Measuring the Curse of Dimensionality and Its Effects on Particle Swarm Optimization and Differential Evolution, 3, Applied Intelligence, 2015. Google Scholar

[17]

J. SunX. WuV. PaladeW. Fang and Y. Shi, Random drift particle swarm optimization algorithm: convergence analysis and parameter selection, Machine Learning, 101 (2015), 345-376.  doi: 10.1007/s10994-015-5522-z.  Google Scholar

[18]

S. V., P. S. K., V. J. and et al, Particle swarm optimization inversion of self-potential data for depth estimation of coal fires over east basuria colliery, jharia coalfield, india, Environmental Earth Sciences, 1-12. Google Scholar

[19]

X. WangJ. Wu and L. Liu, The development of modern service industry in china and its influence factors, Journal of Interdisciplinary Mathematics,, 20 (2008), 161-171.   Google Scholar

[20]

L. Y., Z. Z.H., L. S. and et al, Competitive and cooperative particle swarm optimization with information sharing mechanism for global optimization problems, European Journal of Operational Research, 3 (2015), 370-382. Google Scholar

[21]

H. Q. Yan, Network intrusion small signal detection model based on optimization particle swarm algorithm, Bulletin of Science & Technology, 12 (2015), 193-195.   Google Scholar

[22]

R. Yan, M. A. Xiao-Juan, T. Z. Lian and L. Wang, Research on optimal joint problem of routing and loading in military airlift, Journal of China Academy of Electronics & Information Technology, 7-13. Google Scholar

[23]

B. YaoB. YuP. HuJ. Gao and M. Zhang, An improved particle swarm optimization for carton heterogeneous vehicle routing problem with a collection depot, Annals of Operations Research, 242 (2016), 303-320.  doi: 10.1007/s10479-015-1792-x.  Google Scholar

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
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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