Particle Swarm Optimization With Composite Particles in Dynamic Environments

Liu, Huan; Yang, Shengxiang; Wang, Dingwei

doi:10.1109/tsmcb.2010.2043527

Cited by 126 publications

(1 citation statement)

References 43 publications

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“…This feature makes PSO suitable for functions where the gradient is either unavailable or computationally expensive. Moreover, PSO is easy to implement, has a high efficiency (Shi & Eberhart, 1998), and can be easily applied to a wide range of applications (Aghdam, Mirzaee, Pourmahmood, & Aghababa, in press;Conforth & Meng, 2010;Liu, Yang, & Wang, 2010;Nabizadeh, Faez, Tavassoli, & Rezvanian, 2010;Nabizadeh, Rezvanian, & Meybodi, 2012;Nickabadi et al, 2012;Norouzzadeh, Ahmadzadeh, & Palhang, 2012;Rezaee Jordehi & Jasni, 2013;Soleimani-Pouri et al, 2012;Yazdani, Nasiri, Sepas-Moghaddam, & Meybodi, 2013). There exist various studies that have combined good characteristics of PSO with other optimisation techniques (Gogna & Tayal, 2013).…”

Section: Introductionmentioning

confidence: 99%

An efficient oscillating inertia weight of particle swarm optimisation for tracking optima in dynamic environments

Kordestani

Rezvanian

Meybodi

2015

Journal of Experimental & Theoretical Artificial Intelligen

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One of the effective techniques for improving the rate of convergence in the particle swarm optimisation (PSO) is modifying the inertia weight parameter. This parameter can specify the search area of the swarm in the environment and establish a good balance between the global and local search ability of the particles. Several strategies have been already suggested and well tested for setting the inertia weight in static environments. However, in dynamic environments, the effect of this parameter on increasing the ability of PSO in tracking the changing optimum has been barely considered. In this paper, a time-varying inertia weight, called oscillating triangular inertia weight, is presented and its performance is measured on the moving peaks benchmark (MPB). Experimental results on various dynamic scenarios generated by MPB demonstrate that the proposed strategy has a better capability to adapt with the environmental changes in comparison with other techniques including constant inertia weight and linearly decreasing inertia weight.

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Section: Introductionmentioning

confidence: 99%

An efficient oscillating inertia weight of particle swarm optimisation for tracking optima in dynamic environments

Kordestani

Rezvanian

Meybodi

2015

Journal of Experimental & Theoretical Artificial Intelligen

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Optimal control applications and methods literature survey (NO. 21–22)

2011

Optim Control Appl Methods

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In order to keep readers up-to-date, the journal will contain a list of recently published journal articles relevant to the fields of optimal control, system optimization and applications six times a year. This list is compiled from a wide range of journals. Please note that even though the list has been broadly categorized these classifications are by no means strict, and are there to assist the reader. Please also note that inclusion in the list is not an endorsement of a paper's quality. If you have any suggestions or comments, please e-mail Leonardo Giovanini at lgiovanini@fich.unl.edu.ar.

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A negotiation‐based service selection approach using swarm intelligence and kernel density estimation

Mezni

Sellami

2018

Softw Pract Exp

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Nowadays, the cloud computing environment is becoming a natural choice to deploy and provide Web services that meet user needs. However, many services provide the same functionality and high quality of service (QoS) but different self-adaptive behaviors. In this case, providers' adaptation policies are useful to select services with high QoS and high quality of adaptation (QoA). Existing approaches do not take into account providers' adaptation policies in order to select services with high reputation and high reaction to changes, which is important for the composition of self-adaptive Web services. In order to actively participate to compositions, candidate services must negotiate their self-* capabilities. Moreover, they must evaluate the participation constraints against their capabilities specified in terms of QoS and adaptation policies. This paper exploits a variant of particle swarm optimization and kernel density estimation in the selection of service compositions and the concurrent negotiations of their QoS and QoA capabilities. Selection and negotiation processes are held between intelligent agents, which adopt swarm intelligence techniques for achieving optimal selection and optimal agreement on providers' offers.To resolve unknown autonomic behavior of candidate services, we deal with the lack of such information by predicting the real QoA capabilities of a service through the kernel density estimation technique. Experiments show that our solution is efficient in comparison with several state-of-the-art selection approaches. KEYWORDSkernel density estimation, negotiation, particle swarm optimization, self-* Web service, service selection Over the last decade, service-oriented computing has emerged as a prominent computing paradigm and changed the way software applications are modeled, provisioned, and consumed. 1 Moreover, service-oriented computing has a reciprocal relationship with cloud computing, as one provides the computing of services and the other provides the services of computing. 2 Indeed, the prevalence of cloud computing as a new model for enabling on-demand access to a shared pool of configurable computing resources has encouraged several companies like Microsoft, Amazon, Salesforce, and Google to publish their cloud platforms in order to provide computing resources and cloud data storage centers. This rapid Softw Pract Exper. 2018;48:1285-1311.wileyonlinelibrary.com/journal/spe

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Particle Swarm Optimization With Composite Particles in Dynamic Environments

Cited by 126 publications

References 43 publications

An efficient oscillating inertia weight of particle swarm optimisation for tracking optima in dynamic environments

An efficient oscillating inertia weight of particle swarm optimisation for tracking optima in dynamic environments

Optimal control applications and methods literature survey (NO. 21–22)

A negotiation‐based service selection approach using swarm intelligence and kernel density estimation

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