Multi-Objective Particle Swarm Optimization with time variant inertia and acceleration coefficients

Tripathi, Praveen Kumar; Bandyopadhyay, Sanghamitra; Pal, Sankar K.

doi:10.1016/j.ins.2007.06.018

Cited by 464 publications

(214 citation statements)

References 24 publications

(52 reference statements)

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“…Furthermore, this approach attaches more than the usual significance to the number of iterations, which makes it hard to adjust running time without observing surprising effects on performance. Similar changes to φ g and φ p have been proposed in the literature, typically favoring a linear increase or decrease of these coefficients over time, bounded by a maximum number of iterations [26], [27]. Nonlinear versions based on the same idea have also been proposed [14].…”

Section: Related Workmentioning

confidence: 97%

Simple adaptive cognition for PSO

Monson

2011

2011 IEEE Congress of Evolutionary Computation (CEC)

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Abstract-A simple, effective, computationally cheap, and easily tuned method is presented for improving PSO performance by automatically adapting acceleration coefficients. While this approach can be shown to be effective on its own as a source of swarm diversity on difficult functions, it is also capable of enhancing other adaptive strategies commonly employed with PSO. Significantly, and unlike many other PSO enhancements designed to improve swarm diversity, this approach does not typically harm the performance of the underlying method, allowing it to work well on easy and difficult functions alike.

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Section: Related Workmentioning

confidence: 97%

Simple adaptive cognition for PSO

Monson

2011

2011 IEEE Congress of Evolutionary Computation (CEC)

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“…In earlier papers, these two v alu es were cons idered equ al t o a cons tan t valu e (usually 2). But further studies suggest that these two parameters also to be adjusted [26]. Experiments show that you can get the best results, in spite of adjusting 1 c on 2.5 at the beginning of the search and then its gradual reduction toward 0.5.…”

Section: T T V T C T R P T X T C T R G T X Tmentioning

confidence: 99%

“…Each individual moves in search space with adjustable velocity and keeps the best position gained ever in its memo ry. The best position obtained by all the individuals of the population is transferred between all particles [22][23][24][25][26]. In fact it is supposed that each particle in each mo ment knows about the best position obtained by all the individuals of the population until that mo ment.…”

Section: Objective Functionmentioning

confidence: 99%

Economic Designing of PV/FC/Wind Hybrid System Considering Components Availability

Arabi¹,

Hajibeigy²

2013

IJMECS

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Abstract-This paper presents an Optimizat ion Sizing of a stand-alone PV/FC/Wind Hybrid System (PFWHS) to optimize the sizes of co mponents of PFWHS. Based on PSO algorith m, one optimal sizing method was developed to determine the optimal configuration of system that can achieve the load required power supply probability (Reliab ility) with a minimu m overall cost of energy (OCE).PFW HS costs involve investments, operation and maintenance as well as loss of load costs. The applied wind and radiat ion datasets belong to northwest region (Jolfa, latitude: 38_56, longitude: 45_ 37, altitude: 710, m) of Iran. In this paper the impact of availability of PFWHS co mponents is investigated on optimal sizing. So, to investigating the impact of component availability rate on PFWHS costs and reliability due to failure and repair rate of PFW HS components and uncertainty in wind speed and solar irradiance, t wo scenarios are considered. In first scenario the availab ility of all co mponents is considered 1 and in second scenario isn't considered 1 because of failure and repair rate of co mponents and uncertainty in wind speed and solar irradiance. Obtained results prove that while the overall cost of energy is optimized, the reliability indices are within a satisfactory bound with regard to the reliab ility standards. Also the results indicate that considering co mplete availability of components causes more reliability rate, so to achieve the actual behavior of PFW HS, the rate of co mponents availability must be considered in suitable insight to designer for supply the load.

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“…PSO has been extended to many fields [6,7,8]. But, in practical application, PSO method has the limitations of converging to undesired local solution or premature convergence [9]. Later many improved PSO methods are proposed to solve these problems.…”

Section: Introductionmentioning

confidence: 99%

An Improved Identification Method for Multivariable System

Jin¹,

Wang²,

Khursheed³

et al. 2014

Appl. Math. Inf. Sci.

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Due to many classical identification methods cannot be directly used for closed-loop control system, an improved identification method is proposed to simultaneously identify model parameters and the structure. The improved identification method used the genetic algorithm to estimate the initial search scope for the PSO algorithm, and then used the search result as the initial value of the Rosenbrock algorithm. On the basis the genetic algorithm to estimate is introduced to provide the rough initial search scope for the presented algorithm to improve the validity and accuracy. Simulation results show that compare with the PSO algorithm, the inertia weight variation PSO algorithm and the PSO-SQP algorithm proposed by Qibing Jin et al, the presented algorithm improves the optimizing efficiency of the particle swarm.

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Multi-Objective Particle Swarm Optimization with time variant inertia and acceleration coefficients

Cited by 464 publications

References 24 publications

Simple adaptive cognition for PSO

Simple adaptive cognition for PSO

Economic Designing of PV/FC/Wind Hybrid System Considering Components Availability

An Improved Identification Method for Multivariable System

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