Journal ArticleDOI
Center particle swarm optimization
TLDR
Experimental results show that CenterPSO achieves better performance than LDWPSO, and the two algorithms are extensively compared on three well-known benchmark functions with 10, 20, 30 dimensions.About:
This article is published in Neurocomputing.The article was published on 2007-01-01. It has received 123 citations till now. The article focuses on the topics: Multi-swarm optimization & Particle swarm optimization.read more
Citations
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Journal ArticleDOI
A modified particle swarm optimizer with dynamic adaptation
TL;DR: A modified particle swarm optimization algorithm with dynamic adaptation that remarkably improves the ability of PSO to jump out of the local optima and significantly enhance the convergence precision.
Journal ArticleDOI
A hybrid fireworks optimization method with differential evolution operators
TL;DR: Experimental results show that the DE operators can improve diversity and avoid prematurity effectively, and the hybrid method outperforms both the FA and the DE on the selected benchmark functions.
Journal ArticleDOI
Chaotic particle swarm optimization for data clustering
TL;DR: Results of the robust performance from ACPSO indicate that this method an ideal alternative for solving data clustering problem.
Journal ArticleDOI
Example-based learning particle swarm optimization for continuous optimization
TL;DR: An example-based learning PSO (ELPSO) is proposed to overcome shortcomings of the canonical PSO by keeping a balance between swarm diversity and convergence speed and outperforms all the tested PSO algorithms in terms of both solution quality and convergence time.
Book ChapterDOI
Particle Swarm Optimization
Ke-Lin Du,Mallappa Kumara Swamy +1 more
TL;DR: In this paper, the stagnation problem of PSO is discussed and a discussion of variants of the PSO as well as its variants can be found in Section 5.2.1.
References
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Journal ArticleDOI
Particle swarm optimization
TL;DR: A snapshot of particle swarming from the authors’ perspective, including variations in the algorithm, current and ongoing research, applications and open problems, is included.
Proceedings ArticleDOI
A modified particle swarm optimizer
Yuhui Shi,Russell C. Eberhart +1 more
TL;DR: A new parameter, called inertia weight, is introduced into the original particle swarm optimizer, which resembles a school of flying birds since it adjusts its flying according to its own flying experience and its companions' flying experience.
Journal ArticleDOI
The particle swarm - explosion, stability, and convergence in a multidimensional complex space
M. Clerc,James Kennedy +1 more
TL;DR: This paper analyzes a particle's trajectory as it moves in discrete time, then progresses to the view of it in continuous time, leading to a generalized model of the algorithm, containing a set of coefficients to control the system's convergence tendencies.
Proceedings ArticleDOI
Comparing inertia weights and constriction factors in particle swarm optimization
Russell C. Eberhart,Yuhui Shi +1 more
TL;DR: It is concluded that the best approach is to use the constriction factor while limiting the maximum velocity Vmax to the dynamic range of the variable Xmax on each dimension.
Book
Machine Learning: Neural and Statistical Classification
TL;DR: A survey of previous comparisons and theoretical work descriptions of methods dataset descriptions criteria for comparison and methodology (including validation) empirical results machine learning on machine learning can be found in this article, where the authors also discuss their own work.