2009
DOI: 10.1587/transinf.e92.d.1354
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Particle Swarm Optimization-A Survey

Abstract: SUMMARYParticle Swarm Optimization (PSO) is a search method which utilizes a set of agents that move through the search space to find the global minimum of an objective function. The trajectory of each particle is determined by a simple rule incorporating the current particle velocity and exploration histories of the particle and its neighbors. Since its introduction by Kennedy and Eberhart in 1995, PSO has attracted many researchers due to its search efficiency even for a high dimensional objective function w… Show more

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Cited by 98 publications
(46 citation statements)
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References 30 publications
(38 reference statements)
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“…PSO has been successfully applied to a number of applications owing to its simplicity and attractive search efficiency. Over the past decades, numerous PSO variants have been proposed in order to enhance the performance of the canonical PSO on both exploratory and exploitative search [55], [56]. One category of the PSO variants focuses on enhancing exploitation (convergence) capability, while the other concentrates more on improving the exploration (diversity) capability of PSO.…”
Section: A Particle Swarm Optimization Variantsmentioning
confidence: 99%
“…PSO has been successfully applied to a number of applications owing to its simplicity and attractive search efficiency. Over the past decades, numerous PSO variants have been proposed in order to enhance the performance of the canonical PSO on both exploratory and exploitative search [55], [56]. One category of the PSO variants focuses on enhancing exploitation (convergence) capability, while the other concentrates more on improving the exploration (diversity) capability of PSO.…”
Section: A Particle Swarm Optimization Variantsmentioning
confidence: 99%
“…Where, 1 f and 2 f are unimodal high-dimensional functions. 3 f and 4 f are multimodal high-dimensional functions. Indicated as the reference [9], the theoretical optimal solutions of part test functions are difficult to obtain.…”
Section: Numerical Simulationmentioning
confidence: 99%
“…And chaotic search strengthens the utilization of the chaotic characteristic. To the multimodal high-dimensional functions 3 f , the convergence rate of this algorithm is 1, and the average optimization is theoretical optimization. The results of this paper are superior to the existed results.…”
Section: Copyright ⓒ 2015 Serscmentioning
confidence: 99%
See 1 more Smart Citation
“…Currently, swarm intelligence algorithms are efficiently used to solve complex optimization problems. Most swarm intelligence algorithms are developed by simulation of foraging behavior, migration patterns, or the evolutionary approach in natural species, and these algorithms include the genetic algorithm (GA) [2], particle swarm optimization (PSO) [3], differential evolution (DE) [4], shuffled frog leaping algorithm (SFLA) [5], biogeography-based optimization (BBO) [6], cuckoo search (CS) [7], krill herd algorithm (KH) [8], fruit fly optimization (FFO) [9], pigeon inspired optimization (PIO) [10], invasive weed optimization (IWO) [11], and bat algorithm (BA) [12].…”
Section: Introductionmentioning
confidence: 99%