A Modified Particle Swarm Optimizer for Tracking Dynamic Systems

Zhang, Xuanping; Du, Yuping; Qin, Guoqiang; Jiang, Lu

doi:10.1007/11539902_72

Cited by 11 publications

(6 citation statements)

References 6 publications

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“…=server.getTransition(); (13) if ( < 0 and () ≤ ) then = 0 (14) else if ( > 0 and () ≤ ) then = 1 (15) end if (16) if ( = 0) then (17) RedeployServerList.add(server); (18) end if (19) At the start of experiments, VMs are assigned resources of different orders of magnitude according to the demands. In order to ensure that servers have enough resources to hold virtual machines, we decide that the numbers of servers and VMs are the same in each experiment.…”

Section: Methodsmentioning

confidence: 99%

“…Subsequently, Zhang et al improved this algorithm, added a dynamic weight to prevent too fast convergence which could lead to locally optimal solution, and then added the exploration of the PSO algorithm [16]. Compared to ant colony algorithm [12,17,18], genetic algorithm [7,19,20], and many other intelligent algorithm, PSO algorithm has fewer parameters, faster convergence, simpler code, easier operation, and so on, so it is of high practicability.…”

Section: Introduction To Bpsomentioning

confidence: 99%

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Energy-Aware VM Initial Placement Strategy Based on BPSO in Cloud Computing

Xiong

Zhao

Wang

et al. 2018

Scientific Programming

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In recent years, high energy consumption has gradually become a prominent problem in a data center. With the advent of cloud computing, computing and storage resources are bringing greater challenges to energy consumption. Virtual machine (VM) initial placement plays an important role in affecting the size of energy consumption. In this paper, we use binary particle swarm optimization (BPSO) algorithm to design a VM placement strategy for low energy consumption measured by proposed energy efficiency fitness, and this strategy needs multiple iterations and updates for VM placement. Finally, the strategy proposed in this paper is compared with other four strategies through simulation experiments. The results show that our strategy for VM placement has better performance in reducing energy consumption than the other four strategies, and it can use less active hosts than others.

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Section: Methodsmentioning

confidence: 99%

Section: Introduction To Bpsomentioning

confidence: 99%

Energy-Aware VM Initial Placement Strategy Based on BPSO in Cloud Computing

Xiong

Zhao

Wang

et al. 2018

Scientific Programming

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show abstract

“…In MPSO [9], gbest and pbest are reevaluated at every instant using a time‐varying evaluation function, and the particle velocity is corrected. The velocity is recalculated thoroughly with respect to the positional relations of gbest and pbest at the previous and current instants, which allows accurate adaptation to environmental changes but results in a significant increase in computational complexity.…”

Section: Preservation Strategy For Pbest and Gbestmentioning

confidence: 99%

Adaptive PSO for online identification of time‐varying systems

Nishida

Sakamoto²

2012

Elect Comm in Japan

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Novel adaptive PSO (particle swarm optimization) algorithms called OPSO (online PSO) and ϵPSO are proposed. These algorithms are designed to prevent increasing of calculation cost in order to embed into online systems, and they have high adaptive performances to environmental changes. The efficiency of the proposed PSO algorithms are demonstrated by numerical simulation and the online identification of an actual dynamic system. © 2012 Wiley Periodicals, Inc. Electron Comm Jpn, 95(7): 10–18, 2012; Published online in Wiley Online Library (http://wileyonlinelibrary.com). DOI 10.1002/ecj.11391

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“…In this algorithm, adaptation to environmental changes, with no significant increase in computational complexity, is made possible due to a simple procedure in which pbest is multiplied by a constant slightly greater than 1 for minimum search at the current instant. In addition, in MPSO (Modified PSO) [9], the particles are evaluated at every instant with respect to environmental changes and the move of the optimal solution in order to achieve accurate particle updating. This algorithm increases the computational complexity but provides prompt adaptation to continuous environmental changes.…”

Section: Related Researchmentioning

confidence: 99%

“…PSO was first proposed for solving static optimization problems. Recently, the method was modified to deal with real dynamic systems, and new PSO algorithms can adapt to environmental changes caused by observation noise or time variation of a system, that is, to a varying search space [3–9]. Such PSO algorithms are designed for time‐varying (dynamic) search spaces, and emphasis is placed on strategies for detecting environmental changes and avoiding convergence to local minimum.…”

Section: Introductionmentioning

confidence: 99%