Particle swarm optimization

By James Kennedy, R. Eberhart. 2002

60,739 citations recorded by the source corpus.

A concept for the optimization of nonlinear functions using particle swarm methodology is introduced. The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed. Benchmark testing of the paradigm is described, and applications, including nonlinear function optimization and neural network training, are proposed. The relationships between particle swarm optimization and both artificial life and genetic algorithms are described.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Benchmark and Survey of Automated Machine Learning Frameworks (2019, methodology): “Originally developed to simulate simple social behavior of individuals in a swarm, particle swarms can also be used as an optimizer (Kennedy & Eberhart, 1995).”
Survey on Automated Machine Learning (2019, methodology): “perparameter optimization using a particle swarm (Reynolds, 1987). Originally developed to simulate simple social behavior of individuals in a swarm, particle swarms can also be used as an optimizer (Kennedy and Eberhart, 1995). Inherently, a particle’s position and velocity are dened by continuous ”
Toward Interpretable-AI Policies Using Evolutionary Nonlinear Decision Trees for Discrete-Action Systems (2020, methodology): “Particle swarm optimization [20] is used to generate the interpretable fuzzy rule set in [21] and is demonstrated on classic control problems involving continuous actions.”
A High-Throughput Screening Approach to Discovering Good Forms of Biologically Inspired Visual Representation (2009, neutral): “genetic algorithms [42], simulated annealing [43], and particle swarm techniques [44]).”
Path planning for autonomous mobile robot navigation with ant colony optimization and fuzzy cost function evaluation (2009, unknown): “Representative methodologies in the heuristic classification are the Probabilistic Roadmaps, Rapidly Exploring Random Trees, Neural Networks, Genetic Algorithms [1,7,11], Simulated Annealing, Ant Colony Optimization, Particle Swarm Optimizer [3,6], Stigmergy, Wavelets, Tabu Search, and Fuzzy Logic [”
A Novel Set-Based Particle Swarm Optimization Method for Discrete Optimization Problems (2010, unknown): “…using the following equation: ) ( ) ( j i j j f j j i j i x pbest r c v v i − ⋅ + ← ω (4) where c is a parameter, r j is a random number in [0] [1], and j j fi pbest ) ( means the j th dimension of the PBest position of the particle f i (j). f i (j) is given by first generating a random…”
Elephant Herding Optimization (2015, unknown): “Two of the widely used SI algorithms are particle swarm optimization (PSO) [2- 9] and ant colony optimization (ACO) [10].”
Spider wasp optimizer: a novel meta-heuristic optimization algorithm (2023, unknown): “2017), particle swarm optimization (Kennedy and Eberhart 1995), Wasp Swarm Optimization (Pinto et al.”
A differential evolution particle swarm optimizer for various types of multi-area economic dispatch problems (2016, methodology): “64 The DE [44-45] and PSO [46] techniques are population-based optimization evolutionary 65 algorithms.”
A ranking selection-based particle swarm optimizer for engineering design optimization problems (2008, unknown): “During the past decade, a novel algorithm called particle swarm optimization (PSO) was introduced by Kennedy and Eberhart (1995) , which was inspired by the social behavior of animals such as birds flocking and fish schooling.”
A comparative study of Artificial Bee Colony algorithm (2009, unknown): “A popular swarm-intelligence-based algorithm is the Particle Swarm Optimization (PSO) algorithm which was introduced by Eberhart and Kennedy in 1995 [13].”
A new hybrid PSOGSA algorithm for function optimization (2010, unknown): “PSO is an evolutionary computation technique which is proposed by Kennedy and Eberhart [8,9].”
Chaotic Krill Herd algorithm (2014, unknown): “…various nature-inspired meta-heuristic approaches have been proposed such as evolutionary strategy (ES) [1] [2], particle swarm optimization (PSO) [27] [28] [40] [56], ant colony optimization (ACO) [4], differential evolution (DE) [10] [19] [38], firefly algorithm (FA) [7], biogeography-based…”