Particle swarm variants: standardized convergence analysis

Cleghorn, Christopher Wesley; Engelbrecht, Andries P.

doi:10.1007/s11721-015-0109-7

Cited by 51 publications

(27 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The objective of the PSO is the optimization of the adaptive layers in the ANFIS model such that optimal membership function parameters are obtained. The stopping criterion was selected in such a way that premature convergence and overfitting is avoided [40,41]. The PSO is terminated if maximum number of iterations is exceeded, satisfactory solution is obtained based on the set conditions, or if there is no further improvement in the objective function over a specific number of iterations.…”

Section: Optimisation Of Anfis Model With Psomentioning

confidence: 99%

Estimation of Municipal Solid Waste (MSW) combustion enthalpy for energy recovery

Olatunji¹,

Akinlabi²,

Madushele³

et al. 2019

EAI Endorsed Transactions on Energy Web

View full text Add to dashboard Cite

The global challenges of climate change have been compounded by an unprecedented level of environmental pollution consequent upon the municipal solid waste, MSW generation. Recent advances by researchers and policymakers are focused on sustainable and renewable energy sources which are technologically feasible, environmentally friendly, and economically viable. Waste-to-fuel initiative is therefore highly beneficial to our environment while also improves the socioeconomic well-being the nations. This current study introduces an adaptive neuro-fuzzy inference systems (ANFIS) model optimised with Particle Swarm Optimisation (PSO) algorithm aimed at predicting the enthalpy of combustion of MSW fuel based on the moisture content (H 2 O), Carbon, Hydrogen, Oxygen, Nitrogen, Sulphur, and Ash contents. This model was trained with 86 MSW biomass data and further tested with a new 37 data points. The developed model was observed to performed better in term of the accuracy when compared with other existing models in the literature. The model was evaluated based on some known error estimation. The values of Root Mean Squared Error (RMSE), Mean Absolute Deviation (MAD), Mean Absolute Percentage Error (MAPE), Log Accuracy ratio (LAR), Coefficient of Correlation (CC) were 3.6277, 22.6202, 0.0337, 0.8673 respectively at computation time (CT) of 36.96 secs. Regression analysis was also carried out to determine the level of correlation between the experimental and predicted high heating values (HHV).

show abstract

Section: Optimisation Of Anfis Model With Psomentioning

confidence: 99%

Estimation of Municipal Solid Waste (MSW) combustion enthalpy for energy recovery

Olatunji¹,

Akinlabi²,

Madushele³

et al. 2019

EAI Endorsed Transactions on Energy Web

View full text Add to dashboard Cite

show abstract

“…This section utilizes the method for empirically validating the stability region of PSO variants as proposed by Cleghorn and Engelbrecht [1,3].…”

Section: Empirical Setupmentioning

confidence: 99%

“…To the authors' knowledge this is the first paper to perform stability analysis of a multiobjective PSO. The theoretically derived region for particle stability of MGPSO is also empirically validated utilizing the assumption for free methodology for stability region validation, as presented in [1,3], and used in [2,5].…”

Section: Introductionmentioning

confidence: 99%

Stability Analysis of the Multi-objective Multi-guided Particle Swarm Optimizer

Cleghorn

Scheepers

Engelbrecht

2018

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

At present particle swarm optimizers (PSO) designed for multi-objective optimization have undergone no form of theoretical stability analysis. This paper derives the sufficient and necessary conditions for order-1 and order-2 stability of the recently proposed multi-guided PSO (MGPSO), which was designed specifically for multi-objective optimization. The paper utilizes a recently published theorem for performing stability analysis on PSO variants, which requires minimal modeling assumptions. It is vital for PSO practitioners to know the actual criteria for particle stability of the given PSO variant being used, as it been shown that particle stability has a considerable impact on PSO's performance. This paper empirically validates its theoretical findings by comparing the derived stability criteria against those of an assumption free MGPSO algorithm. It was found that the derived criteria for order-1 and order-2 stability are an accurate predictor of the unsimplified MGPSO's particle behavior.

show abstract

“…In order to verify whether or not newly derived stability criteria are truly representative of the unsimplified PSO variant under consideration, it is still recommended to perform some form of empirical verification of the criteria in an assumption free context. Such an empirical approach is detailed in Cleghorn and Engelbrecht (2015).…”

Section: Direct Application Of Stability Theorymentioning

confidence: 99%

Particle swarm stability: a theoretical extension using the non-stagnate distribution assumption

Cleghorn

Engelbrecht

2017

Swarm Intell

Self Cite

View full text Add to dashboard Cite

This paper presents an extension of the state of the art theoretical model utilized for understanding the stability criteria of the particles in particle swarm optimization algorithms. Conditions for order-1 and order-2 stability are derived by modeling, in the simplest case, the expected value and variance of a particle's personal and neighborhood best positions as convergent sequences of random variables. Furthermore, the condition that the expected value and variance of a particle's personal and neighborhood best positions are convergent sequences is shown to be a necessary condition for order-1 and order-2 stability. The theoretical analysis presented is applicable to a large class of particle swarm optimization variants.

show abstract

Particle swarm variants: standardized convergence analysis

Cited by 51 publications

References 30 publications

Estimation of Municipal Solid Waste (MSW) combustion enthalpy for energy recovery

Estimation of Municipal Solid Waste (MSW) combustion enthalpy for energy recovery

Stability Analysis of the Multi-objective Multi-guided Particle Swarm Optimizer

Particle swarm stability: a theoretical extension using the non-stagnate distribution assumption

Contact Info

Product

Resources

About