Analysis of the Publications on the Applications of  Particle Swarm Optimisation

Poli, Roberto

doi:10.1155/2008/685175

Cited by 547 publications

(377 citation statements)

References 61 publications

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“…So the whole population moves like a single group towards an optimal area. From the implementation perspectives, PSO is easy to implement and computationally efficient compared with other EAs algorithms [31,37].…”

Section: Particle Swarm Optimization Algorithmmentioning

confidence: 99%

“…More specifically, each particle in the swarm has repository memory, which maintains and tracks iteratively the best position the particle has ever visited. Each particle in the swarm is influenced by two environmental factors: one is social behavior presented by the whole swarm, also called a global best; and the second is the personal behavior called personal best [22,31,37,41]. The general procedure of PSO is outlined in Algorithm 1.…”

Section: Particle Swarm Optimization Algorithmmentioning

confidence: 99%

“…The major advantages of PSO compared with other evolutionary algorithms could be summarized as follows: a) PSO is easy to implement and there are few control parameters to be tuned, and b) PSO is conceptually simple, computationally efficient, and robust to control parameters [23,31,37]. PSO has been successfully applied in a wide range of applications such as task assignment problem [40], n-queen problem [29], and power systems [52].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An evolutionary framework using particle swarm optimization for classification method PROAFTN

Al-Obeidat¹,

Belacel²,

Carretero³

et al. 2011

Applied Soft Computing

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The aim of this paper is to introduce a methodology based on the particle swarm optimization (PSO) algorithm to train the Multi-Criteria Decision Aid (MCDA) method PROAFTN. PSO is an efficient evolutionary optimization algorithm using the social behavior of living organisms to explore the search space. It is a relatively new population-based metaheuristic that can be used to find approximate solutions to difficult optimization problems. Furthermore, it is easy to code and robust to control parameters. To apply PROAFTN, the values of several parameters need to be determined prior to classification, such as boundaries of intervals and weights. In this study, the proposed technique is named PSOPRO, which utilizes PSO to elicit the PROAFTN parameters from examples during the learning process. To test the effectiveness of the methodology and the quality of the obtained models, PSOPRO is evaluated on 12 public-domain datasets and compared with the previous work applied on PROAFTN. The computational results demonstrate that PSOPRO is very competitive with respect to the most common classification algorithms.

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Section: Particle Swarm Optimization Algorithmmentioning

confidence: 99%

Section: Particle Swarm Optimization Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An evolutionary framework using particle swarm optimization for classification method PROAFTN

Al-Obeidat¹,

Belacel²,

Carretero³

et al. 2011

Applied Soft Computing

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show abstract

“…The simplicity, flexibility, and good performance of PSO have made it a popular choice as a global problem solver in a wide range of real-world applications such as human tremor analysis (Eberhart & Hu 1999), tracking dynamic systems (Eberhart & Shi 2001), RNA molecule structure prediction (Agrawal & Agrawal 2015), and synthesis of antenna arrays (Ram et al 2014). In many applications where PSO has been used, it has shown consistently good performance (Hu et al 2004;Poli 2008;Yang 2015). Moreover, thanks to its speed, simplicity, and flexibility in formulating problems, PSO has been successfully used in many hybrid algorithms to solve specific problems such as antenna optimization, classification of biological data, and vehicle routing (Robinson et al 2002;Holden & Freitas 2005;Marinakis et al 2010).…”

Section: Introductionmentioning

confidence: 99%

An evolutionary computation based algorithm for calculating solar differential rotation by automatic tracking of coronal bright points

Shahamatnia

Dorotovič

Fonseca

et al. 2016

J. Space Weather Space Clim.

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Developing specialized software tools is essential to support studies of solar activity evolution. With new space missions such as Solar Dynamics Observatory (SDO), solar images are being produced in unprecedented volumes. To capitalize on that huge data availability, the scientific community needs a new generation of software tools for automatic and efficient data processing. In this paper a prototype of a modular framework for solar feature detection, characterization, and tracking is presented. To develop an efficient system capable of automatic solar feature tracking and measuring, a hybrid approach combining specialized image processing, evolutionary optimization, and soft computing algorithms is being followed. The specialized hybrid algorithm for tracking solar features allows automatic feature tracking while gathering characterization details about the tracked features. The hybrid algorithm takes advantages of the snake model, a specialized image processing algorithm widely used in applications such as boundary delineation, image segmentation, and object tracking. Further, it exploits the flexibility and efficiency of Particle Swarm Optimization (PSO), a stochastic population based optimization algorithm. PSO has been used successfully in a wide range of applications including combinatorial optimization, control, clustering, robotics, scheduling, and image processing and video analysis applications. The proposed tool, denoted PSO-Snake model, was already successfully tested in other works for tracking sunspots and coronal bright points. In this work, we discuss the application of the PSO-Snake algorithm for calculating the sidereal rotational angular velocity of the solar corona. To validate the results we compare them with published manual results performed by an expert.

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“…A review of the applications of the PSO is presented in [3] by identifying and analyzing around 700 PSO application papers stored in the IEEE Xplore database.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Bare Bones Particle Swarm Inspired by Cloud Model

Zhang

Yao

et al. 2011

IEICE Trans. Inf. & Syst.

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SUMMARYKennedy has proposed the bare bones particle swarm (BBPS) by the elimination of the velocity formula and its replacement by the Gaussian sampling strategy without parameter tuning. However, a delicate balance between exploitation and exploration is the key to the success of an optimizer. This paper firstly analyzes the sampling distribution in BBPS, based on which we propose an adaptive BBPS inspired by the cloud model (ACM-BBPS). The cloud model adaptively produces a different standard deviation of the Gaussian sampling for each particle according to the evolutionary state in the swarm, which provides an adaptive balance between exploitation and exploration on different objective functions. Meanwhile, the diversity of the swarms is further enhanced by the randomness of the cloud model itself. Experimental results show that the proposed ACM-BBPS achieves faster convergence speed and more accurate solutions than five other contenders on twenty-five unimodal, basic multimodal, extended multimodal and hybrid composition benchmark functions. The diversity enhancement by the randomness in the cloud model itself is also illustrated.

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Analysis of the Publications on the Applications of Particle Swarm Optimisation

Cited by 547 publications

References 61 publications

An evolutionary framework using particle swarm optimization for classification method PROAFTN

An evolutionary framework using particle swarm optimization for classification method PROAFTN

An evolutionary computation based algorithm for calculating solar differential rotation by automatic tracking of coronal bright points

Adaptive Bare Bones Particle Swarm Inspired by Cloud Model

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