Differential evolution particle swarm optimization algorithm for reduction of network loss and voltage instability

Vaisakh, K.; Sridhar, M.; Murthy, K. S. R.

doi:10.1109/nabic.2009.5393308

Cited by 6 publications

(4 citation statements)

References 28 publications

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“…To achieve better performance (convergence speed, accuracy, and global optimization ability) compared to individual methods, several hybrid approaches appear in the literature, including DE and PSO ( Hendtlass, 2001 ; Zhang and Xie, 2003 ; Talbi and Batouche, 2004 ; Hao et al, 2007 ; Das et al, 2008 ; Vaisakh et al, 2009 ; Garcia-Guarin et al, 2019 ; Dash et al, 2020 ; Li et al, 2021 ), PSO and GA ( Mousa et al, 2012 ; Esmin and Matwin, 2013 ; Samuel and Rajan, 2015 ; Ali and Tawhid, 2017 ; Dziwiński and Bartczuk, 2019 ; Pu et al, 2019 ; Omidinasab and Goodarzimehr, 2020 ; Fu et al, 2021 ; Goodarzimehr et al, 2023 ), and GA and DE ( Trivedi et al, 2015 , 2016 ; Abidin, 2018 ; Aguitoni et al, 2018 ; Chaudhary et al, 2019 ; Fathy et al, 2020 ) ( Fig. 9 ).…”

Section: Numerical Solution and Calibrationmentioning

confidence: 99%

Building blocks needed for mechanistic modeling of bioprocesses: A critical review based on protein production by CHO cells

González-Hernández,

Perré

2024

Metabolic Engineering Communications

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Section: Numerical Solution and Calibrationmentioning

confidence: 99%

Building blocks needed for mechanistic modeling of bioprocesses: A critical review based on protein production by CHO cells

González-Hernández,

Perré

2024

Metabolic Engineering Communications

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“…While promising results were obtained with respect to performance and convergence time, it was noted that the DEPSO algorithm could also be applied to the personal best position, instead of the global best. Vaisakh et al [70] came up with a DEPSO algorithm to achieve optimal reactive power dispatch with reduced power and enhanced voltage stability. The IEEE-30 bus test system is used to illustrate its effectiveness and results confirm the superiority of the algorithm proposed.…”

Section: Hybridization Of Pso Using Differential Evolution (De)mentioning

confidence: 99%

Particle Swarm Optimization: A Survey of Historical and Recent Developments with Hybridization Perspectives

Sengupta

Basak

Peters

2018

MAKE

395

184

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Particle Swarm Optimization (PSO) is a metaheuristic global optimization paradigm that has gained prominence in the last two decades due to its ease of application in unsupervised, complex multidimensional problems which cannot be solved using traditional deterministic algorithms. The canonical particle swarm optimizer is based on the flocking behavior and social cooperation of birds and fish schools and draws heavily from the evolutionary behavior of these organisms. This paper serves to provide a thorough survey of the PSO algorithm with special emphasis on the development, deployment and improvements of its most basic as well as some of the very recent state-of-the-art implementations. Concepts and directions on choosing the inertia weight, constriction factor, cognition and social weights and perspectives on convergence, parallelization, elitism, niching and discrete optimization as well as neighborhood topologies are outlined. Hybridization attempts with other evolutionary and swarm paradigms in selected applications are covered and an up-to-date review is put forward for the interested reader.

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“…Author/s: Year Algorithm Area of Application Hendtlass [64] 2001 SDEA Unconstrained Global Optimization Zhang and Xie [65] 2003 DEPSO Unconstrained Global Optimization Talbi and Batouche [66] 2004 DEPSO Rigid-body Multimodal Image Registration Hao et al [67] 2007 DEPSO Unconstrained Global Optimization Das et al [68] 2008 PSO-DV Design Optimization Luitel and Venayagamoorthy [69] 2008 DEPSO Linear Phase FIR Filter Design Vaisakh et al [70] 2009 DEPSO Power Dispatch Huang et al [71] 2009 DEPSO-ParallelFEM Back Analysis of Mechanics Parameters Xu et al [76] 2010 DEPSO Clustering Xiao and Zuo [77] 2012 Multi-DEPSO Dynamic Optimization Junfei et al [78] 2013 DEPSO Mobile Robot Localization Sahu et al [79] 2014 DEPSO PID Controller Seyedmahmoudian et al [80] 2015 DEPSO Photovoltaic Power Generation Gomes and Saraiva [81] 2016 DEPSO Transmission Expansion Planning Boonserm and Sitjongsataporn [82] 2017 DEPSO-Scout Numerical Optimization…”

Section: Hybridization Of Pso Using Genetic Algorithms (Ga)mentioning

confidence: 99%

Particle Swarm Optimization: A survey of Historical and Recent Developments with Hybridization Perspectives

Sengupta¹,

Basak²,

Peters³

2018

Preprint

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Particle Swarm Optimization (PSO) is a metaheuristic global optimization paradigm that has gained prominence in the last two decades due to its ease of application in unsupervised, complex multidimensional problems which cannot be solved using traditional deterministic algorithms. The canonical particle swarm optimizer is based on the flocking behavior and social co-operation of birds and fish schools and draws heavily from the evolutionary behavior of these organisms. This paper serves to provide a thorough survey of the PSO algorithm with special emphasis on the development, deployment and improvements of its most basic as well as some of the very recent state–of-the-art implementations. Concepts and directions on choosing the inertia weight, constriction factor, cognition and social weights and perspectives on convergence, parallelization, elitism, niching and discrete optimization as well as neighborhood topologies are outlined. Hybridization attempts with other evolutionary and swarm paradigms in selected applications are covered and an up-to-date review is put forward for the interested reader.

show abstract

Differential evolution particle swarm optimization algorithm for reduction of network loss and voltage instability

Cited by 6 publications

References 28 publications

Building blocks needed for mechanistic modeling of bioprocesses: A critical review based on protein production by CHO cells

Building blocks needed for mechanistic modeling of bioprocesses: A critical review based on protein production by CHO cells

Particle Swarm Optimization: A Survey of Historical and Recent Developments with Hybridization Perspectives

Particle Swarm Optimization: A survey of Historical and Recent Developments with Hybridization Perspectives

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