A program to demonstrate the optimization process of particle swarm optimization. A two-dimensional objective function is visualized by level of grey: the lighter the color, the higher the function value. The particles are shown as red circles, their trajectory as red lines.
An unsatisfactory property of particle filters is that they
may become inefficient when the observation noise is low.
In this paper we consider a simple-to-implement particle filter,
called ‘LIS-based particle filter’, whose aim is to overcome
the above mentioned weakness. LIS-based particle
filters sample the particles in a two-stage process that uses
information of the most recent observation, too. Experiments
with the standard bearings-only tracking problem indicate
that the proposed new particle filter method is indeed
a viable alternative to other methods.
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％ set some variables in the workspace to control behaviour:
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％ graphicsMode 0 no graphics,
％ 1 graphics, particles, beacons, ground truth
％ 2 graphics, as above + range circles
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％ perfect 0 use beacon range data
％ 1 use ground truth range data
％ clear a
非常好的優(yōu)化算法的書，詳細介紹了蟻群算法和粒子群算法以及相關(guān)的matlab工具箱，講了理論和應(yīng)用給出了工具箱的下載地址。
Swarm intelligence is an innovative computational way to solve hard problems. In particular, particle swarm optimization, also commonly known as PSO, mimics the behavior of a swarm of insects or a school of fish. If one of the particle discovers a good path to food the rest of the swarm will be able to follow instantly even if they are far away in the swarm. Swarm behavior is modeled by particles in multidimensional space that have two characteristics: a position and a velocity. These particles wander around the hyperspace and remember the best position that they have discovered. They communicate good positions to each other and adjust their own position and velocity based on these good positions.
The VGA example generates a 320x240 diffusion-limited-aggregation (DLA) on Altera DE2 board. A DLA is a clump formed by sticky particles adhering to an existing structure. In this design, we start with one pixel at the center of the screen and allow a random walker to bounce around the screen until it hits the pixel at the center. It then sticks and a new walker is started randomly at one of the 4 corners of the screen. The random number generators for x and y steps are XOR feedback shift registers (see also Hamblen, Appendix A). The VGA driver, PLL, and reset controller from the DE2 CDROM are necessary to compile this example. Note that you must push KEY0 to start the state machine.
DDSCAT 7.3 is a freely available open-source Fortran-90 software package applying the “discrete
dipole approximation” (DDA) to calculate scattering and absorption of electromagnetic waves by targets
with arbitrary geometries and complex refractive index. The targets may be isolated entities (e.g., dust
particles), but may also be 1-d or 2-d periodic arrays of “target unit cells”, which can be used to study
absorption, scattering, and electric ?elds around arrays of nanostructures.
