Parameter selection in particle swarm optimisation: a survey

Jordehi, Ahmad Rezaee; Jasni, Jasronita

doi:10.1080/0952813x.2013.782348

Cited by 260 publications

(70 citation statements)

References 8 publications

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“…Although there are some studies that show that values of inertia weight between 0.4 and 0.9 are widely accepted in the literature [28], the simulations were carried out with values from 0.1 to 1.7 to have more data to contrast with the results of these studies. The goal of these simulations was to obtain the highest energy efficiency index for the installation at the same time that the level of the convergence of the particles was studied.…”

Section: Methodsmentioning

confidence: 89%

Particle Swarm Optimization for Outdoor Lighting Design

et al. 2017

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Abstract:Outdoor lighting is an essential service for modern life. However, the high influence of this type of facility on energy consumption makes it necessary to take extra care in the design phase. Therefore, this manuscript describes an algorithm to help light designers to get, in an easy way, the best configuration parameters and to improve energy efficiency, while ensuring a minimum level of overall uniformity. To make this possible, we used a particle swarm optimization (PSO) algorithm. These algorithms are well established, and are simple and effective to solve optimization problems. To take into account the most influential parameters on lighting and energy efficiency, 500 simulations were performed using DIALux software (4.10.0.2, DIAL, Ludenscheid, Germany). Next, the relation between these parameters was studied using to data mining software. Subsequently, we conducted two experiments for setting parameters that enabled the best configuration algorithm in order to improve efficiency in the proposed process optimization.

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Section: Methodsmentioning

confidence: 89%

Particle Swarm Optimization for Outdoor Lighting Design

et al. 2017

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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: 94%

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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“…It utilizes a population of particles to represent candidate solutions in a search space, and optimizes the problem by iteration to move these particles to the best solutions with regard to a given measure of quality. Compared with the above algorithms, the advantages of particle swarm optimization are the following [15,16,17]. …”

Section: Introductionmentioning

confidence: 99%

“…However, they do not consider the recently-proposed PSO-based localization algorithms, nor do they give parameter selections. The existing PSO’s parameter selection guidelines [16] are not based on the objective function in localization problem of WSN, so these parameters cannot achieve the optimal localization performance.…”

Section: Introductionmentioning

confidence: 99%

Parameter Selection and Performance Comparison of Particle Swarm Optimization in Sensor Networks Localization

Cui

Shu

Song

et al. 2017

Sensors

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Localization is a key technology in wireless sensor networks. Faced with the challenges of the sensors’ memory, computational constraints, and limited energy, particle swarm optimization has been widely applied in the localization of wireless sensor networks, demonstrating better performance than other optimization methods. In particle swarm optimization-based localization algorithms, the variants and parameters should be chosen elaborately to achieve the best performance. However, there is a lack of guidance on how to choose these variants and parameters. Further, there is no comprehensive performance comparison among particle swarm optimization algorithms. The main contribution of this paper is three-fold. First, it surveys the popular particle swarm optimization variants and particle swarm optimization-based localization algorithms for wireless sensor networks. Secondly, it presents parameter selection of nine particle swarm optimization variants and six types of swarm topologies by extensive simulations. Thirdly, it comprehensively compares the performance of these algorithms. The results show that the particle swarm optimization with constriction coefficient using ring topology outperforms other variants and swarm topologies, and it performs better than the second-order cone programming algorithm.

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Parameter selection in particle swarm optimisation: a survey

Cited by 260 publications

References 8 publications

Particle Swarm Optimization for Outdoor Lighting Design

Particle Swarm Optimization for Outdoor Lighting Design

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

Parameter Selection and Performance Comparison of Particle Swarm Optimization in Sensor Networks Localization

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