Particle-Swarm-Optimization
This paper presents an overview of our most recent results concerning the Particle
Swarm Optimization (PSO) method. Techniques for the alleviation of local minima, and for
detecting multiple minimizers are described. Moreover, results on the ability of the PSO in
tackling Multiobjective, Minimax, Integer Programming and 1 errors-in-variables problems,
as well as problems in noisy and continuously changing environments, are reported. Finally,
a Composite PSO, in which the heuristic parameters of PSO are controlled by a Differential
Evolution algorithm during the optimization, is described, and results for many well-known
and widely used test functions are given.
- 2015-03-11 20:43:28下载
- 积分:1
gloptipoly3
GloptiPoly 3: moments, optimization and
semidefinite programming.
Gloptipoly 3 is intended to solve, or at least approximate, the Generalized Problem of
Moments (GPM), an infinite-dimensional optimization problem which can be viewed as
an extension of the classical problem of moments [8]. From a theoretical viewpoint, the
GPM has developments and impact in various areas of mathematics such as algebra,
Fourier analysis, functional analysis, operator theory, probability and statistics, to cite
a few. In addition, and despite a rather simple and short formulation, the GPM has a
large number of important applications in various fields such as optimization, probability,
finance, control, signal processing, chemistry, cristallography, tomography, etc. For an
account of various methodologies as well as some of potential applications, the interested
reader is referred to [1, 2] and the nice collection of papers [5].(GloptiPoly 3: moments, optimization andsemidefinite programming.Gloptipoly 3 is intended to solve, or at least approximate, the Generalized Problem ofMoments (GPM), an infinite-dimensional optimization problem which can be viewed asan extension of the classical problem of moments [8] . From a theoretical viewpoint, theGPM has developments and impact in various areas of mathematics such as algebra, Fourier analysis, functional analysis, operator theory, probability and statistics, to citea few. In addition, and despite a rather simple and short formulation, the GPM has alarge number of important applications in various fields such as optimization, probability, finance, control, signal processing, chemistry, cristallography, tomography, etc. For anaccount of various methodologies as well as some of potential applications, the interestedreader is referred to [ 1, 2] and the nice collection of papers [5].)
- 2008-04-28 20:26:36下载
- 积分:1