Particle Swarm Optimization Research Toolbox (Scripts) Publisher's description
from George Evers
The Particle Swarm Optimization Research Toolbox was written to assist with thesis research combating the premature convergence problem of particle swarm optimization (PSO)
The Particle Swarm Optimization Research Toolbox was written to assist with thesis research combating the premature convergence problem of particle swarm optimization (PSO). The control panel offers ample flexibility to accommodate various research directions. After specifying your intentions, the toolbox will automate several tasks to make time for conceptual planning.
+ Choose from Gbest PSO, Lbest PSO, RegPSO, GCPSO, MPSO, OPSO, Cauchy mutation of global best, and hybrid combinations.
+ The benchmark suite consists of Ackley, Griewangk, Quadric, noisy Quartic, Rastrigin, Rosenbrock, Schaffer's f6, Schwefel, Sphere, and Weighted Sphere.
+ Each trial number maps to a unique sequence of pseudo-random numbers to ensure both replicability and uniqueness of data.
+ Specify either a maximum number of function evaluations or iterations with the options to terminate early if the threshold for success is reached or premature convergence is detected.
+ Select either a static or linearly varying inertia weight, and specify the value(s).
+ Activate velocity clamping and specify the percentage.
+ Choose symmetric or asymmetric initialization.
+ A suite of pre-made graph types facilitates understanding of swarm behavior.
AUTOMATED GRAPH FEATURES
> Automatically generate titles, legends, and labels.
> Automatically save figures to any supported format.
> Specify where on the screen to generate figures.
> Phase plots trace each particle's path across a contour map of the search space with update numbers overlaid.*
> Swarm trajectory snapshots capture the swarm state in intervals with optional tags marking global and personal bests. *
> The global bests's function value vs iteration shows how solution quality progresses and stagnates over the course of the search.
> The global best vs iteration shows how each decision variable progresses and stagnates with time.
> Each particle's function value vs iteration shows how its own solution quality oscillates with time.
> Each particle's position vector vs iteration shows how its decision variables oscillate toward a local or global minimizer.
> Each particle's velocity vector vs iteration shows how velocity components diminish with time.
> Each particle's personal best vs iteration showss both the regularity and significance of updates.
* Note: Graph types marked with an asterisk are for 2D optimization problems by nature of the contour map.
+ Confine particles to the initialization space when physical limitations or a priori knowledge mandate doing so; but if the initialization space is merely an educated guess at an unfamiliar application problem, particles can be allowed to roam outside.
+ Specify which of the following histories to maintain in order to control execution speed and the size of automatically saved workspaces.
> Global bests
> Function values of global bests
> Personal bests
> Function values of personal bests
> Function values of positions
> Cognitive velocity components
> Social velocity components
Note: Disabling lengthy histories is recommended except when generating data to be published or verifying proper toolbox functioning, in which case the histories should be analyzed.
+ Automatic input validation assertively corrects conflicting settings and notifies of changes made.
+ Automatically save the workspace after each trial and set of trials.
+ Automatically generate statistics.
+ Free yourself from the computer with a progress meter estimating completion time. A "choo choo" sound conveniently signals completion.
+ An Introductory Walk-through teaches the basic functionalities of the toolbox, including how to analyze data.
+ ANN Training Add-in by Tricia Rambharose
System Requirements:MATLAB 7.8 (R2009a)
Program Release Status: Major Update
Program Install Support: Install and Uninstall