✦ LIBER ✦

The particle swarm optimization algorithm: convergence analysis and parameter selection

✍ Scribed by Ioan Cristian Trelea

Publisher: Elsevier Science
Year: 2003
Tongue: English
Weight: 162 KB
Volume: 85
Category: Article
ISSN: 0020-0190
DOI: 10.1016/s0020-0190(02)00447-7

No coin nor oath required. For personal study only.

✦ Synopsis

The particle swarm optimization algorithm is analyzed using standard results from the dynamic system theory. Graphical parameter selection guidelines are derived. The exploration–exploitation tradeoff is discussed and illustrated. Examples of performance on benchmark functions superior to previously published results are given.

📜 SIMILAR VOLUMES

Parameter selection and adaptation in Un

Parameter selection and adaptation in Unified Particle Swarm Optimization

✍ K.E. Parsopoulos; M.N. Vrahatis 📂 Article 📅 2007 🏛 Elsevier Science 🌐 English ⚖ 655 KB

The performance of the recently proposed Unified Particle Swarm Optimization method is investigated under different schemes for the determination and adaptation of the unification factor, which is the main parameter of the method, controlling its exploration and exploitation properties. Widely used

Parallel global optimization with the pa

Parallel global optimization with the particle swarm algorithm

✍ J. F. Schutte; J. A. Reinbolt; B. J. Fregly; R. T. Haftka; A. D. George 📂 Article 📅 2004 🏛 John Wiley and Sons 🌐 English ⚖ 291 KB

Decay rate and l2 gain analysis for the

Decay rate and l2 gain analysis for the particle swarm optimization algorithm

✍ Yuji Wakasa; Kanya Tanaka; Takuya Akashi; Yuki Nishimura 📂 Article 📅 2010 🏛 John Wiley and Sons 🌐 English ⚖ 326 KB 👁 1 views

The behavior of the particle swarm optimization (PSO) algorithm is analyzed by regarding its dynamics as a system with multiplicative noise and applying control-theoretic analysis methods. In order to evaluate the convergence and diversity of the PSO algorithm, two new measures related to the decay

Analysis of uniform circular arrays for

Analysis of uniform circular arrays for adaptive beamforming applications using particle swarm optimization algorithm

✍ Korany R. Mahmoud; Mohamed I. Eladawy; Rajeev Bansal; Saber H. Zainud-Deen; Sabr 📂 Article 📅 2007 🏛 John Wiley and Sons 🌐 English ⚖ 575 KB

In this article, the particle swarm optimization algorithm is used to calculate the complex excitations, amplitudes and phases, of the adaptive circular array elements. To illustrate the performance of this method for steering a signal in the desired direction and imposing nulls in the direction of

Parameters optimization of a nano-partic

Parameters optimization of a nano-particle wet milling process using the Taguchi method, response surface method and genetic algorithm

✍ Tung-Hsu Hou; Chi-Hung Su; Wang-Lin Liu 📂 Article 📅 2007 🏛 Elsevier Science 🌐 English ⚖ 759 KB

Nano-particles have been successfully and widely applied in many industrial applications. The wet-type mechanical milling process is a popular method used to produce nano-particles. Therefore, it is very important to improve milling process capability and quality by setting the optimal milling param

On the selection of auxiliary functions,

On the selection of auxiliary functions, operators, and convergence control parameters in the application of the Homotopy Analysis Method to nonlinear differential equations: A general approach

✍ Robert A. Van Gorder; K. Vajravelu 📂 Article 📅 2009 🏛 Elsevier Science 🌐 English ⚖ 226 KB

The Homotopy Analysis Method of Liao [Liao SJ. Beyond perturbation: introduction to the Homotopy Analysis Method. Boca Raton: Chapman & Hall/CRC Press; 2003] has proven useful in obtaining analytical solutions to various nonlinear differential equations. In this method, one has great freedom to sele