Hybrid Particle Swarm Optimization Algorithm and Firefly Algorithm Based Combined Economic and Emission Dispatch Including Valve Point Effect

Arunachalam, S.; AgnesBhomila, T.; Babu, M. Ramesh

doi:10.1007/978-3-319-20294-5_56

Cited by 25 publications

(20 citation statements)

References 23 publications

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“…Constrained Optimization Zhao et al [146] 2014 HCPSO-IA Complex layout design problems Arunachalam et al [151] 2014 HPSOFF Combined Economic and Emission Dispatch Problem Pan et al [148] 2015 Hybrid PSO-BA Global Optimization Manoj et al [149] 2016 PSO-BA Medical Image Registration Xia et al [150] 2017 FAPSO Global Optimization…”

Section: Author/smentioning

confidence: 99%

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

Sengupta

Basak

Peters

2018

MAKE

394

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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Section: Author/smentioning

confidence: 99%

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

Sengupta

Basak

Peters

2018

MAKE

394

184

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“…al. in [15]. There is no alteration to firefly algorithm in the proposed algorithm but the initial solution that is used by FA is obtained from PSO.…”

Section: Hybrid Firefly Algorithm and Particle Swarm Optimizationmentioning

confidence: 99%

Comparison of Hybrid Firefly Algorithms for Power Allocation in a TV White Space Network

Kennedy¹,

Kamucha²,

Tonny³

2019

IJCA

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TV white spaces (TVWS) can be used by Secondary Users (SUs) through Dynamic Spectrum Access (DSA) as long as they do not cause harmful interference to Primary Users (PUs). Due spectrum scarcity, there is increasing demand for DSA. When there is a high density of SUs in a TVWS network such as cellular access to TVWS, problem of interference among SUs will arise. Possibility of harmful interference to PUs may also arise. Optimization of power allocation is therefore necessary to reduce the level of interference among SUs and to protect PUs against harmful interference. Performance of different hybrid firefly algorithm with particle swarm optimization and genetic algorithm for optimization of power allocation in a TVWS network are compared. Simulation was done using Matlab. Simulation results show that hybrid of firefly algorithm, particle swarm optimization and genetic algorithm outperform other hybrid firefly algorithms. Hybrid of firefly algorithm, particle swarm optimization and genetic algorithm achieves best throughput, sum power as well as objective function value.

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“…Author/s: Year Algorithm Area of Application Shi et al [152] 2012 HEPGO Global Optimization El-Shirbiny and Alhamali [147] 2013 HPSIL Fixed Charge Transportation Problems Liu and Zhou [153] 2013 New (GSO-PSO) Constrained Optimization Zhao et al [146] 2014 HCPSO-IA Complex layout design problems Arunachalam et al [151] 2014 HPSOFF Combined Economic and Emission Dispatch Problem Pan et al [148] 2015 Hybrid PSO-BA Global Optimization Manoj et al [149] 2016 PSO-BA Medical Image Registration Xia et al [150] 2017 FAPSO Global Optimization…”

Section: Glow Worm Swarm Optimization (Gso)mentioning

confidence: 99%

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

Sengupta¹,

Basak²,

Peters³

2018

Preprint

View full text Add to dashboard Cite

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

Hybrid Particle Swarm Optimization Algorithm and Firefly Algorithm Based Combined Economic and Emission Dispatch Including Valve Point Effect

Cited by 25 publications

References 23 publications

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

Comparison of Hybrid Firefly Algorithms for Power Allocation in a TV White Space Network

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

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