Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems

By Yamille del Valle, Ganesh Kumar Venayagamoorthy, Salman Mohagheghi, J. Hernandez, Ronald G. Harley. IEEE Transactions on Evolutionary Computation, 2008

2,324 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/d01c3787de501024dcad7d2227fa255e9945e9db

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In Electrical and Electronic Engineering

Citations in the corpus

Computational Intelligence in Wireless Sensor Networks: A Survey (2011, methodology): “Several variants of PSO have been devised [39] and applied to optimization problems in power systems, stock This article has been accepted for inclusion in a future issue of this journal.”
Particle swarm optimization algorithm: an overview (2017, background): “Avoidingprematureconvergenceonalocaloptimumsolu-tion, meanwhile keeping the fast convergence feature of the original PSO formulation, is an important reason why a few PSO variants have been put forward (Valle et al. 2008).”
Rational approximation for fractional-order system by particle swarm optimization (2012, neutral): “timization problem [22].”
Considering Carbon Capture and Storage in Electricity Generation Expansion Planning (2016, methodology): “2, Pbest(t) and gbest(t) are the local and global best solutions, pid(t) and vid(t) are the population and velocity matrixes in iteration k [20].”
Plug-in Vehicles and Renewable Energy Sources for Cost and Emission Reductions (2011, background): “Binary PSO is used to determine the optimal on/off states of conventional generating units.”
Resource Scheduling Under Uncertainty in a Smart Grid With Renewables and Plug-in Vehicles (2012, methodology): “PSO is used to calculate the schedule, power dispatch and corresponding emissions.”
A conceptual comparison of the Cuckoo-search, particle swarm optimization, differential evolution and artificial bee colony algorithms (2011, methodology): “…impossible or extremely difficult by using the classical optimization techniques (Karaboga and Akay 2009a; Nowak and Cirpka 2004; Clerc and Kennedy 2002; Trelea 2003; Dorigo et al. 1996; Ong et al. 2006; Storn and Price 1997; Yang and Deb 2010; Das et al. 2011; Yang 2005, 2009; Zhang et al. 2007).”
A Survey of Multi-Objective Optimization in Wireless Sensor Networks: Metrics, Algorithms, and Open Problems (2016, background): “Then, the particles are manipulated according to the following two equations [208]:”
Surrogate-Assisted Cooperative Swarm Optimization of High-Dimensional Expensive Problems (2017, methodology): “METAHEURISTIC optimization algorithms, such as genetic algorithms, differential evolution, ant colony optimization, and particle swarm optimization (PSO), have been empirically shown to perform well on many realworld optimization problems, ranging from job shop scheduling [1], [2], power systems [3]”
Particle Swarm Optimization With an Aging Leader and Challengers (2013, methodology): “As PSO is simple in concept and effective to explore global solutions for some hard problems, PSO has become one of the most popular optimization techniques and has been successfully applied in many application areas such as task assignment [3], power systems [4], [5], and biomedical image registrat”
Genetic Learning Particle Swarm Optimization (2016, background): “In the next generation, suppose that pbest and gbest are updated to Pi = [5, 4] and G = [2, 1], respectively, and the newly calculated exemplar is Ei = [4, 3].”
Survey of Robot 3D Path Planning Algorithms (2016, background): “PSO High time complexity [75] It acts faster than GA and can deal with a low number of individuals problems Premature convergence”
Particle Swarm Optimization in Wireless-Sensor Networks: A Brief Survey (2011, background): “4) Availability of variants for real, integer, and binary domains [4].”
Group Search Optimizer: An Optimization Algorithm Inspired by Animal Searching Behavior (2009, background): “Interested readers are directed to an excellent survey of recent development of PSO [5].”