Synchronous parallelization of Particle Swarm Optimization with digital pheromones

Kalivarapu, Vijay; Foo, Jung-Leng; Winer, Eliot H.

doi:10.1016/j.advengsoft.2009.04.002

Cited by 20 publications

(10 citation statements)

References 45 publications

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“…Surisunthon presented impacts of distributed generation on voltage sag assessment in distribution systems. Voltage sag assessment is analyzed by using the method of fault positions in order to determine area of vulnerability (AOV), voltage sag frequency and voltage sag index [14].…”

Section: Stagementioning

confidence: 99%

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Optimal DG Allocation and Sizing for Mitigating Voltage Sag in Distribution Systems Using Particle Swarm Optimization

Sharma¹

2013

IOSRJEN

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-The necessity for clean electric power without any power quality problems has persuaded distribution companies to overcome these issues. Sag/swell, flicker and harmonics are these ones. Among them, voltage sag is one of the most important problems. This phenomenon imposes a bad effect on domestic and industrial loads, especially on sensitive ones. An alternative is use of distributed generation (DG) because of its positive impacts such as voltage support, improved reliability, small size and losses reduction, together with improving voltage sag. An approach is proposed based on Particle Swarm Optimization (PSO), capable to establish the optimal DG allocation and sizing on a distribution network. The objective function is the summation of cost of injecting energy of DGs, fixed installation cost of DGs and number of percentage of buses Experiencing voltage sag in a vulnerable zone that multi plied by number of faults for every kind of faults. The results show that the appropriate placement of DG results in improvement of the aforementioned indices.

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Section: Stagementioning

confidence: 99%

“…It has quick convergence speed and optimal searching ability for solving large-scale optimization problems [14]. The PSO-based approach for solving OPDG problem to minimize the loss takes the following steps:…”

Section: The Pso Algorithm Proceduresmentioning

confidence: 99%

Optimal DG Allocation and Sizing for Mitigating Voltage Sag in Distribution Systems Using Particle Swarm Optimization

Sharma¹

2013

IOSRJEN

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“…It is a star topology, in which each particle has access only to the information of all other particles in the swarm, as shown in Figure 1(a). The solution characteristics, parallel speed-up and efficiency as well as maintaining load balance between processors can be further improved with other parallelisation schemes (Venter and Sobieszczanski-Sobieski, 2006;Koh et al, 2006;Kalivarapu et al, 2009). The two most common topologies are the wheel topology in which the individuals are isolated from one another as information has to be communicated through a focal individual, see Figure 1(b), and the ring topology in which each particle connected to two neighbours, as shown in Figure 1(c).…”

Section: Topology Of the Particle Swarmmentioning

confidence: 99%

Reservoir Model History Matching With Particle Swarms

Mohamed

Christie

Demyanov

2010

SPE Oil and Gas India Conference and Exhibition

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History matching optimisation in Bayesian framework is a fairly recent approach to quantify uncertainty in oil industry. Currently some innovative global optimisation approaches such as evolutionary algorithms and swarm intelligence methods have gained popularity for tackling history matching problems.Particle swarm optimisation (PSO) is a swarm intelligence approach for solving optimisation problems. In this approach particles are moving points in parameter space. The position of a particle is a candidate solution to the optimisation problem. Each particle searches for better positions in parameter space by updating its velocity according to rules originally inspired by behavioural models of the movement of flocks of birds.Recently the PSO algorithm has shown to be a promising tool for finding acceptable multiple history matched models quickly (Mohamed et al., 2009). However, the algorithm control parameters which balance between exploitation-exploration trade-off are to be studied for history matching problems. In this paper we investigate some basic PSO variants for updating the control parameters using a real-life case study.It is shown that PSO could be improved by optimising the PSO control parameters. Some variants converge faster to good fitting regions in parameter space leading to a fewer number of reservoir simulation runs though others maintain diversity of the reservoir models better for this reservoir example. This study helps better employing the PSO algorithm for reservoir model history matching and uncertainty quantification.

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“…However, the selection of the training parameters of SVM has a heavy impact on the performance of SVM. Particle swarm optimization (PSO) is proposed Kennedy & Eberhart in 1995(dos Santos Coelho & Augusto Sierakowski, 2008Jarboui, Ibrahim, Siarry, & Rebai, 2008;Kalivarapu, Foo, & Winer, 2009), which is inspired by social behavior among individuals like the birds blocking or the fish grouping. The method can efficiently find optimal or near-optimal solutions in large search spaces (Cura, 2009;Sun, 2009).…”

Section: Introductionmentioning

confidence: 99%

Face recognition method based on support vector machine and particle swarm optimization

Jin

Zhang

2011

Expert Systems with Applications

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Synchronous parallelization of Particle Swarm Optimization with digital pheromones

Cited by 20 publications

References 45 publications

Optimal DG Allocation and Sizing for Mitigating Voltage Sag in Distribution Systems Using Particle Swarm Optimization

Optimal DG Allocation and Sizing for Mitigating Voltage Sag in Distribution Systems Using Particle Swarm Optimization

Reservoir Model History Matching With Particle Swarms

Face recognition method based on support vector machine and particle swarm optimization

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