|
Particle Swarm Optimization Research Toolbox 1.0 File ID: 78603 |
---|
|
| Particle Swarm Optimization Research Toolbox 1.0 License: Freeware File Size: 1.9 MB Downloads: 493
Submit Rating: |
|
|
|
Particle Swarm Optimization Research Toolbox 1.0 Description |
---|
Description: 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.
EXAMPLE FEATURES
+ 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. GRAPH TYPES > 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. ITERATIVE HISTORIES > Global bests > Function values of global bests > Personal bests > Function values of personal bests > Positions > Function values of positions > Velocities > 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.
ADD-IN + ANN Training Add-in by Tricia Rambharose http://www.mathworks.com/matlabcentral/fileexchange/29565 License: Freeware Related: allowed, ample flexibility, asterisk, after specifying, after each, activate velocity, Addin, educated, introductory, labelsgt, notifies, Hybrid, analyzed automatic, unfamiliar, analyze dataaddin, ackley griewangk, accommodate, optional tags, Options, aut O/S:BSD, Linux, Solaris, Mac OS X File Size: 1.9 MB Downloads: 493
|
|
More Similar Code |
---|
An animated simulation of Particles in 2D searching for a global minima of a simple function using Particle Swarm Optimization algorithm
Particle swarm optimization is a technique used in many control systems application. Here i used the PSO in PID controller tuning
This upload contains a hybrid Particle Swarm Optimization algorithm for functions in the real space. An options file is also provided, which lets the user fully parameterize the process. The hybrid function used is the @fminsearch, which is...
A new hybrid population-based algorithm (PSOGSA) is proposed with the combination of Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA). The main idea is to integrate the ability of exploitation in PSO...
Particle swarm optimization is a derivative-free global optimum search algorithm based on the collective intelligence of a large group of intercommunicating entities. The individual particles are simple and primitive, knowing only their own...
Base paper detail : "Improved Particle Swarm Optimization Based Load Frequency Control In A Single Area Power System" Saumya Kr. Gautam, Nakul Goyal Department of Electrical Engineering, IT-BHU Varanasi,India.
This add-in to the PSO Research toolbox (Evers 2009) aims to allow an artificial neural network (ANN or simply NN) to be trained using the Particle Swarm Optimization (PSO) technique (Kennedy, Eberhart et al. 2001).
This add-in acts...
A few popular metaheuristic algorithms are included, such as the particle swarm optimization, firefly algorithm, harmony search and others.
In the global optimization and GADS toolboxes, "ackleyfcn.m" implements a different Ackley formulation than what is commonly used in optimization literature. For those who prefer the traditional benchmark formulation, this fix modifies...
Implements various optimization methods which do not use the gradient of the problem being optimized, including Particle Swarm Optimization, Differential Evolution, and others. The implementation is simple and easy to understand.
Also... |
User Review for Particle Swarm Optimization Research Toolbox |
|