A new optimizer using particle swarm theory

By R. Eberhart, James Kennedy. 1995

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Citations in the corpus

Accessorize to a Crime: Real and Stealthy Attacks on State-of-the-Art Face Recognition (2016, methodology): “Our algorithm for attacking Face++ uses particle swarm optimization (PSO) [6] as a subroutine.”
Fire Hawk Optimizer: a novel metaheuristic algorithm (2022, methodology): “PSO, for example, is one of the pioneer algorithms in the metaheuristic area, in which the position updating process by the solution candidates is conducted using the global best and local best of each particle.”
Grey wolf optimizer: a review of recent variants and applications (2017, methodology): “SI algorithms include many algorithms such ant colony optimization (ACO) [13], particle swarm optimization (PSO) [15], cuckoo search (CS) [138], krill herd optimization (KH) [29], firefly algorithm (FA) [137], artificial bee colony (ABC) [50], multi-verse optimizer (MVO) [82], ant lion optimizer (AL”
Hybrid Nelder-Mead simplex search and particle swarm optimization for constrained engineering design problems (2009, background): “Particle swarm optimization (PSO) is one of the latest evolutionary optimization techniques developed by Eberhart and Kennedy (1995). PSO concept is based on a metaphor of social interaction such as bird flocking and fish schooling.”
Multiobjective robust optimization method for drawbead design in sheet metal forming (2010, methodology): “Inspired by the emergent motion of a flock of birds and fish searching for food, the particle swarm optimization (PSO) method was introduced by Kennedy and Eberhart [35].”
INFO: An efficient optimization algorithm based on weighted mean of vectors (2022, background): “Particle swarm optimization (PSO) is one of the most successful algorithms in this class, which was inspired by birds' social and individual intelligence when flocking [18].”
Hybrid Particle Swarm Optimization Approach for Solving the Discrete OPF Problem Considering the Valve Loading Effects (2007, background): “was first introduced primitively by Eberhart and Kennedy in 1995 [14], [15].”
A particle swarm optimization algorithm for makespan and total flowtime minimization in the permutation flowshop sequencing problem (2007, methodology): “Since PSO was first introduced to optimize various continuous nonlinear functions by Kennedy and Eberhart [8,20], it has been successfully applied to a wide range of applications such as power and voltage control [58,1], neural network training [52], mass-spring system [5], task assignment [42], sup”